All Countries Regional Data
AIM: Create region by region analysis of male vs female for each age groups (0-4, 5-14, 0-14 and 15 plus) for all countries separated by region
library(dplyr)
library(tidyverse)
library(tidyr)
library(forecast)
Registered S3 method overwritten by 'quantmod':
method from
as.zoo.data.frame zoo library(fpp2)── Attaching packages ─────────────────────────────────────────────────── fpp2 2.4 ──
✓ fma 2.4 ✓ expsmooth 2.3
── Conflicts ────────────────────────────────────────────────────── fpp2_conflicts ──
x magrittr::extract() masks tidyr::extract()
x magrittr::set_names() masks purrr::set_names()library(stringr)
library(magrittr)
library(ggplot2)Organising the data by g_whoregion
grouped_2013 <- group_by(world_2013, g_whoregion) %>% filter(year == 2013) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)),
newrel_f04 = sum(na.omit(newrel_f04)),
newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)),
newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)),
newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2013)
grouped_2014 <- group_by(world_2013, g_whoregion) %>% filter(year == 2014) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)),
newrel_f04 = sum(na.omit(newrel_f04)),
newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)),
newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)),
newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2014)
grouped_2015 <- group_by(world_2013, g_whoregion) %>% filter(year == 2015) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)),
newrel_f04 = sum(na.omit(newrel_f04)),
newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)),
newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)),
newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2015)
grouped_2016 <- group_by(world_2013, g_whoregion) %>% filter(year == 2016) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)),
newrel_f04 = sum(na.omit(newrel_f04)),
newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)),
newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)),
newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2016)
grouped_2017 <- group_by(world_2013, g_whoregion) %>% filter(year == 2017) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)),
newrel_f04 = sum(na.omit(newrel_f04)),
newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)),
newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)),
newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2017)
grouped_2018 <- group_by(world_2013, g_whoregion) %>% filter(year == 2018) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)),
newrel_f04 = sum(na.omit(newrel_f04)),
newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)),
newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)),
newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2018)
grouped_2019 <- group_by(world_2013, g_whoregion) %>% filter(year == 2019) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)),
newrel_f04 = sum(na.omit(newrel_f04)),
newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)),
newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)),
newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2019)
grouped_2020 <- group_by(world_2013, g_whoregion) %>% filter(year == 2020) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)),
newrel_f04 = sum(na.omit(newrel_f04)),
newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)),
newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)),
newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2020)
bind_world <- rbind(grouped_2013, grouped_2014, grouped_2015, grouped_2016, grouped_2017, grouped_2018, grouped_2019, grouped_2020)
bind_world <- bind_world[, c(1, 10, 2:9)]
arrange_world <- arrange(bind_world, g_whoregion)
##Including columns for total values
mutate_world <- mutate(arrange_world, "newrel_tot04" = newrel_m04 + newrel_f04, "newrel_tot514" = newrel_m514 + newrel_f514, "newrel_tot014" = newrel_m014 + newrel_f014, "newrel_tot15plus" = newrel_m15plus + newrel_f15plus)
perc_world <- mutate(mutate_world, "perc_014" = 100 * (newrel_tot014/(newrel_tot014 + newrel_tot15plus)), "perc_youngkids" = 100 * (newrel_tot04/newrel_tot014))
Creating graphs for male vs female for 0-4 by g_whoregion
afr_04 <- perc_world %>% filter(g_whoregion == "AFR") %>% select(newrel_m04, newrel_f04) %>%
t()
colnames(afr_04) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
afr_04 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,30000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-4 years in AFR")numeric(0)
amr_04 <- perc_world %>% filter(g_whoregion == "AMR") %>% select(newrel_m04, newrel_f04) %>%
t()
colnames(amr_04) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
amr_04 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,3000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-4 years in AMR")numeric(0)
emr_04 <- perc_world %>% filter(g_whoregion == "EMR") %>% select(newrel_m04, newrel_f04) %>%
t()
colnames(emr_04) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
emr_04 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,20000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-4 years in EMR")numeric(0)
eur_04 <- perc_world %>% filter(g_whoregion == "EUR") %>% select(newrel_m04, newrel_f04) %>%
t()
colnames(eur_04) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
eur_04 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,3000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-4 years in EUR")numeric(0)
sea_04 <- perc_world %>% filter(g_whoregion == "SEA") %>% select(newrel_m04, newrel_f04) %>%
t()
colnames(sea_04) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
sea_04 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,60000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-4 years in SEA")numeric(0)
wpr_04 <- perc_world %>% filter(g_whoregion == "WPR") %>% select(newrel_m04, newrel_f04) %>%
t()
colnames(wpr_04) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
wpr_04 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,20000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-4 years in WPR")numeric(0)
Creating graphs for notifications in 5-14 years by g_whoregion
afr_514 <- perc_world %>% filter(g_whoregion == "AFR") %>% select(newrel_m514, newrel_f514) %>%
t()
colnames(afr_514) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
afr_514 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,40000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-514 years in AFR")numeric(0)
amr_514 <- perc_world %>% filter(g_whoregion == "AMR") %>% select(newrel_m514, newrel_f514) %>%
t()
colnames(afr_514) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
amr_514 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,4000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 5-14 years in AMR")numeric(0)
emr_514 <- perc_world %>% filter(g_whoregion == "EMR") %>% select(newrel_m514, newrel_f514) %>%
t()
colnames(emr_514) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
emr_514 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,30000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 5-14 years in EMR")numeric(0)
eur_514 <- perc_world %>% filter(g_whoregion == "EUR") %>% select(newrel_m514, newrel_f514) %>%
t()
colnames(eur_514) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
eur_514 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,5000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 5-14 years in EUR")numeric(0)
sea_514 <- perc_world %>% filter(g_whoregion == "SEA") %>% select(newrel_m514, newrel_f514) %>%
t()
colnames(sea_514) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
sea_514 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,100000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 5-14 years in SEA")numeric(0)
wpr_514 <- perc_world %>% filter(g_whoregion == "WPR") %>% select(newrel_m514, newrel_f514) %>%
t()
colnames(wpr_514) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
wpr_514 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,30000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 5-14 years in WPR")numeric(0)
Creating plot for 0-14
afr_014 <- perc_world %>% filter(g_whoregion == "AFR") %>% select(newrel_m014, newrel_f014) %>%
t()
colnames(afr_014) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
afr_014 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,70000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-14 years in AFR")numeric(0)
amr_014 <- perc_world %>% filter(g_whoregion == "AMR") %>% select(newrel_m014, newrel_f014) %>%
t()
colnames(amr_014) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
amr_014 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,6000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-14 years in AMR")numeric(0)
emr_014 <- perc_world %>% filter(g_whoregion == "EMR") %>% select(newrel_m014, newrel_f014) %>%
t()
colnames(emr_014) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
emr_014 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,50000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-14 years in EMR")numeric(0)
eur_014 <- perc_world %>% filter(g_whoregion == "EUR") %>% select(newrel_m014, newrel_f014) %>%
t()
colnames(eur_014) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
eur_014 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,8000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-14 years in EUR")numeric(0)
sea_014 <- perc_world %>% filter(g_whoregion == "SEA") %>% select(newrel_m014, newrel_f014) %>%
t()
colnames(sea_014) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
sea_014 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,150000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-14 years in SEA")numeric(0)
wpr_014 <- perc_world %>% filter(g_whoregion == "WPR") %>% select(newrel_m014, newrel_f014) %>%
t()
colnames(wpr_014) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
wpr_014 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,50000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-14 years in WPR")numeric(0)
Creating plot for 15 plus
afr_15plus <- perc_world %>% filter(g_whoregion == "AFR") %>% select(newrel_m15plus, newrel_f15plus) %>%
t()
colnames(afr_15plus) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
afr_15plus %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,1000000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 15plus years in AFR")numeric(0)
amr_15plus <- perc_world %>% filter(g_whoregion == "AMR") %>% select(newrel_m15plus, newrel_f15plus) %>%
t()
colnames(amr_15plus) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
amr_15plus %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,300000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 15 plus years in AMR")numeric(0)
emr_15plus <- perc_world %>% filter(g_whoregion == "EMR") %>% select(newrel_m15plus, newrel_f15plus) %>%
t()
colnames(emr_15plus) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
emr_15plus %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,500000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 15plus years in EMR")numeric(0)
eur_15plus <- perc_world %>% filter(g_whoregion == "EUR") %>% select(newrel_m15plus, newrel_f15plus) %>%
t()
colnames(eur_15plus) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
eur_15plus %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,200000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 15plus years in EUR")numeric(0)
sea_15plus <- perc_world %>% filter(g_whoregion == "SEA") %>% select(newrel_m15plus, newrel_f15plus) %>%
t()
colnames(sea_15plus) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
sea_15plus %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,2000000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 15plus years in SEA")numeric(0)
wpr_15plus <- perc_world %>% filter(g_whoregion == "WPR") %>% select(newrel_m15plus, newrel_f15plus) %>%
t()
colnames(wpr_15plus) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
wpr_15plus %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,1000000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 15plus years in WPR")numeric(0)
Plotting total notifications over time
tot_world <- mutate(perc_world, "newrel_mtot" = newrel_m014 + newrel_m15plus, "newrel_ftot" = newrel_f014 + newrel_f15plus, "TOT" = newrel_mtot + newrel_ftot)
afr_tot <- tot_world %>% filter(g_whoregion == "AFR") %>% select(newrel_mtot, newrel_ftot) %>%
t()
colnames(afr_tot) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
afr_tot %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,1000000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for all ages years in AFR")numeric(0)
amr_tot <- tot_world %>% filter(g_whoregion == "AMR") %>% select(newrel_mtot, newrel_ftot) %>%
t()
colnames(amr_tot) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
amr_tot %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,200000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for all ages years in AMR")numeric(0)
emr_tot <- tot_world %>% filter(g_whoregion == "EMR") %>% select(newrel_mtot, newrel_ftot) %>%
t()
colnames(emr_tot) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
emr_tot %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,500000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for all ages years in EMR")numeric(0)
eur_tot <- tot_world %>% filter(g_whoregion == "EUR") %>% select(newrel_mtot, newrel_ftot) %>%
t()
colnames(eur_tot) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
eur_tot %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,200000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for all ages years in EUR")numeric(0)
sea_tot <- tot_world %>% filter(g_whoregion == "SEA") %>% select(newrel_mtot, newrel_ftot) %>%
t()
colnames(sea_tot) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
sea_tot %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,3000000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for all ages years in SEA")numeric(0)
wpr_tot <- tot_world %>% filter(g_whoregion == "WPR") %>% select(newrel_mtot, newrel_ftot) %>%
t()
colnames(wpr_tot) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
wpr_tot %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,1500000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for all ages years in WPR")numeric(0)
##Attempt at creating a forecast
filt_afr04 <- perc_world %>% filter(g_whoregion == "AFR") %>% filter(year != 2020)
ts_afr04 <- ts(filt_afr04[, c(3)], start = 2013, frequency = 1)
arima_afr04 <- auto.arima(ts_afr04, d=1, D=1, stepwise = FALSE, approximation = FALSE, trace = TRUE)
ARIMA(0,1,0) : 111.1694
ARIMA(0,1,0) with drift : 114.7184
ARIMA(0,1,1) : 115.9016
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 115.7354
ARIMA(1,1,0) with drift : Inf
ARIMA(1,1,1) : 125.4818
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : Inf
ARIMA(2,1,0) with drift : Inf
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0) print(summary(arima_afr04))Series: ts_afr04
ARIMA(0,1,0)
sigma^2 estimated as 3954567: log likelihood=-54.08
AIC=110.17 AICc=111.17 BIC=109.96
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 793.0733 1841.094 1358.216 3.037032 5.197868 0.8590867 -0.650155checkresiduals(arima_afr04)
Ljung-Box test
data: Residuals from ARIMA(0,1,0)
Q* = 5.0245, df = 3, p-value = 0.17
Model df: 0. Total lags used: 3
fcst_afrm04 <- forecast(arima_afr04, h = 1)
autoplot(fcst)
sum_afrm04 <- print(summary(fcst))
Forecast method: ARIMA(0,1,0)
Model Information:
Series: ts_afr04
ARIMA(0,1,0)
sigma^2 estimated as 3954567: log likelihood=-54.08
AIC=110.17 AICc=111.17 BIC=109.96
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 793.0733 1841.094 1358.216 3.037032 5.197868 0.8590867 -0.650155
Forecasts:####Attempt at creating function to produce predictions
out_2020 <- function(df = tot_world, region){
df %>% filter(year != 2020) %>% filter(g_whoregion == region) %>% return() }
arima_TB <- function(df = tot_world, group) {
data1 <- df %>% select(group) %>% ts(start = 2013, frequency = 1) %>% auto.arima(d=1, D=1, stepwise = FALSE, approximation = FALSE, trace = TRUE)
print(summary(data1))
FCST <- forecast(data1, h = 1)
autoplot(FCST)
print(summary(FCST))}
##AFRICA ESTIMATES
afr_m04 <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_m04") Note: Using an external vector in selections is ambiguous.
ℹ Use `all_of(group)` instead of `group` to silence this message.
ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
This message is displayed once per session.
ARIMA(0,1,0) : 111.1694
ARIMA(0,1,0) with drift : 114.7184
ARIMA(0,1,1) : 115.9016
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 115.7354
ARIMA(1,1,0) with drift : Inf
ARIMA(1,1,1) : 125.4818
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : Inf
ARIMA(2,1,0) with drift : Inf
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 3954567: log likelihood=-54.08
AIC=110.17 AICc=111.17 BIC=109.96
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 793.0733 1841.094 1358.216 3.037032 5.197868 0.8590867 -0.650155
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 3954567: log likelihood=-54.08
AIC=110.17 AICc=111.17 BIC=109.96
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 793.0733 1841.094 1358.216 3.037032 5.197868 0.8590867 -0.650155
Forecasts:rownames(afr_m04) <- "afr_m04"
afr_f04 <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_f04")
ARIMA(0,1,0) : 111.1038
ARIMA(0,1,0) with drift : 114.5687
ARIMA(0,1,1) : 116.0674
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 116.0417
ARIMA(1,1,0) with drift : 123.1058
ARIMA(1,1,1) : Inf
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : Inf
ARIMA(2,1,0) with drift : Inf
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 3911516: log likelihood=-54.05
AIC=110.1 AICc=111.1 BIC=109.9
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 808.0217 1831.046 1337.165 3.504448 6.329902 0.8588083 -0.4289628
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 3911516: log likelihood=-54.05
AIC=110.1 AICc=111.1 BIC=109.9
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 808.0217 1831.046 1337.165 3.504448 6.329902 0.8588083 -0.4289628
Forecasts:rownames(afr_f04) <- "afr_f04"
afr_m514 <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_m514")
ARIMA(0,1,0) : 117.6737
ARIMA(0,1,0) with drift : 121.1402
ARIMA(0,1,1) : 122.6736
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 122.6736
ARIMA(1,1,0) with drift : 130.0079
ARIMA(1,1,1) : 132.6046
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 131.7788
ARIMA(2,1,0) with drift : 159.9311
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 11691922: log likelihood=-57.34
AIC=116.67 AICc=117.67 BIC=116.47
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 1395.087 3165.699 2467.373 4.502111 8.326399 0.8582664 -0.3285392
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 11691922: log likelihood=-57.34
AIC=116.67 AICc=117.67 BIC=116.47
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 1395.087 3165.699 2467.373 4.502111 8.326399 0.8582664 -0.3285392
Forecasts:rownames(afr_m514) <- "afr_m514"
afr_f514 <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_f514")
ARIMA(0,1,0) : 117.2334
ARIMA(0,1,0) with drift : 121.2201
ARIMA(0,1,1) : 122.2328
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 122.2325
ARIMA(1,1,0) with drift : 130.5247
ARIMA(1,1,1) : Inf
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 131.8107
ARIMA(2,1,0) with drift : 160.4149
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 10864754: log likelihood=-57.12
AIC=116.23 AICc=117.23 BIC=116.03
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 1117.231 3051.663 2274.088 3.379023 7.511132 0.8584703 -0.2394624
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 10864754: log likelihood=-57.12
AIC=116.23 AICc=117.23 BIC=116.03
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 1117.231 3051.663 2274.088 3.379023 7.511132 0.8584703 -0.2394624
Forecasts:rownames(afr_f514) <- "afr_f514"
afr_m014 <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_m014")
ARIMA(0,1,0) : 121.8118
ARIMA(0,1,0) with drift : 125.8302
ARIMA(0,1,1) : 126.7857
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 126.7703
ARIMA(1,1,0) with drift : 134.2116
ARIMA(1,1,1) : 136.3851
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 135.8829
ARIMA(2,1,0) with drift : Inf
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 23303291: log likelihood=-59.41
AIC=120.81 AICc=121.81 BIC=120.6
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 1614.328 4469.256 3188.614 2.684558 5.574168 0.8590016 -0.3180155
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 23303291: log likelihood=-59.41
AIC=120.81 AICc=121.81 BIC=120.6
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 1614.328 4469.256 3188.614 2.684558 5.574168 0.8590016 -0.3180155
Forecasts:rownames(afr_m014) <- "afr_m014"
afr_f014 <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_f014")
ARIMA(0,1,0) : 122.4939
ARIMA(0,1,0) with drift : 126.8435
ARIMA(0,1,1) : 127.4896
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 127.488
ARIMA(1,1,0) with drift : 136.419
ARIMA(1,1,1) : Inf
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 137.1726
ARIMA(2,1,0) with drift : 166.4068
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 26109056: log likelihood=-59.75
AIC=121.49 AICc=122.49 BIC=121.29
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 1410.503 4730.665 3787.646 2.301644 7.442093 0.8586494 -0.1756356
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 26109056: log likelihood=-59.75
AIC=121.49 AICc=122.49 BIC=121.29
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 1410.503 4730.665 3787.646 2.301644 7.442093 0.8586494 -0.1756356
Forecasts:rownames(afr_f014) <- "afr_f014"
afr_m15plus <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_m15plus")
ARIMA(0,1,0) : 147.8467
ARIMA(0,1,0) with drift : 151.7651
ARIMA(0,1,1) : 152.7979
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 152.7548
ARIMA(1,1,0) with drift : 159.8714
ARIMA(1,1,1) : Inf
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 161.2934
ARIMA(2,1,0) with drift : 189.8465
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 1.786e+09: log likelihood=-72.42
AIC=146.85 AICc=147.85 BIC=146.64
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 14791.42 39126.57 26895.42 2.246912 4.075435 0.8596953 -0.3986632
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 1.786e+09: log likelihood=-72.42
AIC=146.85 AICc=147.85 BIC=146.64
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 14791.42 39126.57 26895.42 2.246912 4.075435 0.8596953 -0.3986632
Forecasts:rownames(afr_m15plus) <- "afr_m15plus"
afr_f15plus <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_f15plus")
ARIMA(0,1,0) : 143.4497
ARIMA(0,1,0) with drift : 148.2692
ARIMA(0,1,1) : 147.7646
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : 157.6537
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 147.4938
ARIMA(1,1,0) with drift : 156.2684
ARIMA(1,1,1) : Inf
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 157.1647
ARIMA(2,1,0) with drift : 186.183
ARIMA(2,1,1) : 186.7743
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 858280286: log likelihood=-70.22
AIC=142.45 AICc=143.45 BIC=142.24
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 4379.362 27123.22 20044.79 0.8601614 4.6618 0.859572 -0.4409411
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 858280286: log likelihood=-70.22
AIC=142.45 AICc=143.45 BIC=142.24
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 4379.362 27123.22 20044.79 0.8601614 4.6618 0.859572 -0.4409411
Forecasts:rownames(afr_f15plus) <- "afr_f15plus"
afr_estimates <- rbind(afr_m04, afr_f04, afr_m514, afr_f514, afr_m014, afr_f014, afr_m15plus, afr_f15plus)
#SEA predictions
sea_m04 <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_m04")
ARIMA(0,1,0) : 129.8095
ARIMA(0,1,0) with drift : 128.9832
ARIMA(0,1,1) : 133.4638
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 133.3583
ARIMA(1,1,0) with drift : 138.4097
ARIMA(1,1,1) : 143.2038
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 143.3582
ARIMA(2,1,0) with drift : 165.0954
ARIMA(2,1,1) : 173.2038
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0) with drift
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
7409.833
s.e. 2361.681
sigma^2 estimated as 40157453: log likelihood=-60.49
AIC=124.98 AICc=128.98 BIC=124.57
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -0.0466907 5355.735 4222.19 0.4100235 14.29603 0.569809 -0.2163359
Forecast method: ARIMA(0,1,0) with drift
Model Information:
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
7409.833
s.e. 2361.681
sigma^2 estimated as 40157453: log likelihood=-60.49
AIC=124.98 AICc=128.98 BIC=124.57
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -0.0466907 5355.735 4222.19 0.4100235 14.29603 0.569809 -0.2163359
Forecasts:rownames(sea_m04) <- "sea_m04"
sea_f04 <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_f04")
ARIMA(0,1,0) : 126.2488
ARIMA(0,1,0) with drift : 125.9369
ARIMA(0,1,1) : 130.0851
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 129.9836
ARIMA(1,1,0) with drift : 135.4326
ARIMA(1,1,1) : 139.8509
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 139.9764
ARIMA(2,1,0) with drift : 162.569
ARIMA(2,1,1) : 169.8484
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0) with drift
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
5355.00
s.e. 1832.19
sigma^2 estimated as 24169343: log likelihood=-58.97
AIC=121.94 AICc=125.94 BIC=121.52
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 0.1219997 4154.975 3249.836 0.6199827 13.78026 0.6068789 -0.1951322
Forecast method: ARIMA(0,1,0) with drift
Model Information:
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
5355.00
s.e. 1832.19
sigma^2 estimated as 24169343: log likelihood=-58.97
AIC=121.94 AICc=125.94 BIC=121.52
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 0.1219997 4154.975 3249.836 0.6199827 13.78026 0.6068789 -0.1951322
Forecasts:rownames(sea_f04) <- "sea_f04"
sea_m514 <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_m514")
ARIMA(0,1,0) : 138.7575
ARIMA(0,1,0) with drift : 141.1449
ARIMA(0,1,1) : 143.4767
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : 153.3661
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 143.3658
ARIMA(1,1,0) with drift : 151.0966
ARIMA(1,1,1) : 153.2438
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : 183.3643
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : Inf
ARIMA(2,1,0) with drift : 180.866
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 392626539: log likelihood=-67.88
AIC=137.76 AICc=138.76 BIC=137.55
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 10092.84 18344.95 10092.84 16.02839 16.02839 0.8572872 -0.2886571
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 392626539: log likelihood=-67.88
AIC=137.76 AICc=138.76 BIC=137.55
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 10092.84 18344.95 10092.84 16.02839 16.02839 0.8572872 -0.2886571
Forecasts:rownames(sea_m514) <- "sea_m514"
sea_f514 <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_f514")
ARIMA(0,1,0) : 139.9886
ARIMA(0,1,0) with drift : 142.6269
ARIMA(0,1,1) : 144.7946
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : 154.2335
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 144.6597
ARIMA(1,1,0) with drift : 152.6186
ARIMA(1,1,1) : 154.6524
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : 184.2124
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 154.649
ARIMA(2,1,0) with drift : 182.54
ARIMA(2,1,1) : 184.4943
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 482053005: log likelihood=-68.49
AIC=138.99 AICc=139.99 BIC=138.78
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 10736.71 20327.03 11456.99 15.81939 16.8546 0.8572705 -0.2512842
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 482053005: log likelihood=-68.49
AIC=138.99 AICc=139.99 BIC=138.78
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 10736.71 20327.03 11456.99 15.81939 16.8546 0.8572705 -0.2512842
Forecasts:rownames(sea_f514) <- "sea_f514"
sea_m014 <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_m014")
ARIMA(0,1,0) : 143.1301
ARIMA(0,1,0) with drift : 144.5331
ARIMA(0,1,1) : 147.643
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : 157.5019
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 147.499
ARIMA(1,1,0) with drift : 154.2417
ARIMA(1,1,1) : Inf
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : 186.9908
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 157.4961
ARIMA(2,1,0) with drift : 183.4944
ARIMA(2,1,1) : 187.4946
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 813725760: log likelihood=-70.07
AIC=142.13 AICc=143.13 BIC=141.92
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 16421.31 26409.83 16421.31 17.32705 17.32705 0.8572857 -0.387646
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 813725760: log likelihood=-70.07
AIC=142.13 AICc=143.13 BIC=141.92
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 16421.31 26409.83 16421.31 17.32705 17.32705 0.8572857 -0.387646
Forecasts:rownames(sea_m014) <- "sea_m014"
sea_f014 <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_f014")
ARIMA(0,1,0) : 143.0538
ARIMA(0,1,0) with drift : 145.0229
ARIMA(0,1,1) : 147.7415
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : 157.2544
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 147.5565
ARIMA(1,1,0) with drift : 154.9293
ARIMA(1,1,1) : Inf
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : 187.2272
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 157.5482
ARIMA(2,1,0) with drift : 184.6836
ARIMA(2,1,1) : 187.3948
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 803450152: log likelihood=-70.03
AIC=142.05 AICc=143.05 BIC=141.85
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 15302.9 26242.55 15302.9 16.59731 16.59731 0.8572895 -0.3224274
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 803450152: log likelihood=-70.03
AIC=142.05 AICc=143.05 BIC=141.85
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 15302.9 26242.55 15302.9 16.59731 16.59731 0.8572895 -0.3224274
Forecasts:rownames(sea_f014) <- "sea_f014"
sea_m15plus <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_m15plus")
ARIMA(0,1,0) : 176.6742
ARIMA(0,1,0) with drift : 179.3839
ARIMA(0,1,1) : 181.6143
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : 191.2782
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 181.5536
ARIMA(1,1,0) with drift : 189.2337
ARIMA(1,1,1) : 191.3233
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : 221.1653
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 191.4064
ARIMA(2,1,0) with drift : 219.2186
ARIMA(2,1,1) : 221.2795
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 2.18e+11: log likelihood=-86.84
AIC=175.67 AICc=176.67 BIC=175.47
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 225490.8 432281.4 237748.2 14.63527 15.40864 0.8573248 -0.3031529
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 2.18e+11: log likelihood=-86.84
AIC=175.67 AICc=176.67 BIC=175.47
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 225490.8 432281.4 237748.2 14.63527 15.40864 0.8573248 -0.3031529
Forecasts:rownames(sea_m15plus) <- "sea_m15plus"
sea_f15plus <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_f15plus")
ARIMA(0,1,0) : 168.8186
ARIMA(0,1,0) with drift : 170.5037
ARIMA(0,1,1) : 173.6346
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 173.459
ARIMA(1,1,0) with drift : 180.1729
ARIMA(1,1,1) : Inf
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 183.2908
ARIMA(2,1,0) with drift : 210.0185
ARIMA(2,1,1) : 213.0905
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 5.887e+10: log likelihood=-82.91
AIC=167.82 AICc=168.82 BIC=167.61
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 135527 224627.7 135527 15.54276 15.54276 0.8573545 -0.3868371
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 5.887e+10: log likelihood=-82.91
AIC=167.82 AICc=168.82 BIC=167.61
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 135527 224627.7 135527 15.54276 15.54276 0.8573545 -0.3868371
Forecasts:rownames(sea_f15plus) <- "sea_f15plus"
sea_estimates <- rbind(sea_m04, sea_f04, sea_m514, sea_f514, sea_m014, sea_f014, sea_m15plus, sea_f15plus)
#WPR estimates
wpr_m04 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m04")
ARIMA(0,1,0) : 117.987
ARIMA(0,1,0) with drift : 120.6596
ARIMA(0,1,1) : 122.6039
ARIMA(0,1,1) with drift : 130.6566
ARIMA(0,1,2) : 132.1259
ARIMA(0,1,2) with drift : 160.3447
ARIMA(1,1,0) : 122.4011
ARIMA(1,1,0) with drift : 130.6558
ARIMA(1,1,1) : 132.1779
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : 161.9353
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 131.972
ARIMA(2,1,0) with drift : 160.5433
ARIMA(2,1,1) : 161.9623
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 12318696: log likelihood=-57.49
AIC=116.99 AICc=117.99 BIC=116.78
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 1705.945 3249.443 2805.374 20.95097 30.38595 0.8571698 -0.1090836
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 12318696: log likelihood=-57.49
AIC=116.99 AICc=117.99 BIC=116.78
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 1705.945 3249.443 2805.374 20.95097 30.38595 0.8571698 -0.1090836
Forecasts:rownames(wpr_m04) <- "wpr_m04"
wpr_f04 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f04")
ARIMA(0,1,0) : 115.7528
ARIMA(0,1,0) with drift : 118.5332
ARIMA(0,1,1) : 120.4465
ARIMA(0,1,1) with drift : 128.5172
ARIMA(0,1,2) : 130.0029
ARIMA(0,1,2) with drift : 158.2683
ARIMA(1,1,0) : 120.2961
ARIMA(1,1,0) with drift : 128.5132
ARIMA(1,1,1) : 130.083
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : 159.8347
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 129.8787
ARIMA(2,1,0) with drift : 158.4132
ARIMA(2,1,1) : 159.8769
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 8488853: log likelihood=-56.38
AIC=114.75 AICc=115.75 BIC=114.54
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 1388.774 2697.436 2351.631 21.47588 31.76637 0.8571647 -0.1220096
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 8488853: log likelihood=-56.38
AIC=114.75 AICc=115.75 BIC=114.54
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 1388.774 2697.436 2351.631 21.47588 31.76637 0.8571647 -0.1220096
Forecasts:rownames(wpr_f04) <- "wpr_f04"
wpr_m514 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m514")
ARIMA(0,1,0) : 121.504
ARIMA(0,1,0) with drift : 123.8299
ARIMA(0,1,1) : 126.3435
ARIMA(0,1,1) with drift : 133.7516
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 126.0875
ARIMA(1,1,0) with drift : 133.6501
ARIMA(1,1,1) : 135.4448
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : Inf
ARIMA(2,1,0) with drift : Inf
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 22137609: log likelihood=-59.25
AIC=120.5 AICc=121.5 BIC=120.3
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 2418.811 4356.041 3571.382 17.62742 23.71631 0.8572347 -0.263211
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 22137609: log likelihood=-59.25
AIC=120.5 AICc=121.5 BIC=120.3
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 2418.811 4356.041 3571.382 17.62742 23.71631 0.8572347 -0.263211
Forecasts:rownames(wpr_m514) <- "wpr_m514"
wpr_f514 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f514")
ARIMA(0,1,0) : 119.8234
ARIMA(0,1,0) with drift : 122.1114
ARIMA(0,1,1) : 124.7307
ARIMA(0,1,1) with drift : 131.9174
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 124.5719
ARIMA(1,1,0) with drift : 131.6532
ARIMA(1,1,1) : 133.8761
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : Inf
ARIMA(2,1,0) with drift : Inf
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 16729524: log likelihood=-58.41
AIC=118.82 AICc=119.82 BIC=118.62
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 2114.529 3786.765 3049.1 17.08469 22.6313 0.8572513 -0.3350534
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 16729524: log likelihood=-58.41
AIC=118.82 AICc=119.82 BIC=118.62
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 2114.529 3786.765 3049.1 17.08469 22.6313 0.8572513 -0.3350534
Forecasts:rownames(wpr_f514) <- "wpr_f514"
wpr_m014 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m014")
ARIMA(0,1,0) : 127.329
ARIMA(0,1,0) with drift : 129.3522
ARIMA(0,1,1) : 132.0091
ARIMA(0,1,1) with drift : 139.3173
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 131.6732
ARIMA(1,1,0) with drift : 139.2927
ARIMA(1,1,1) : 141.1479
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 140.1957
ARIMA(2,1,0) with drift : 168.6569
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 58446545: log likelihood=-62.16
AIC=126.33 AICc=127.33 BIC=126.12
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 4098.811 7077.926 6008.525 18.28722 24.50654 0.8572381 -0.1974907
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 58446545: log likelihood=-62.16
AIC=126.33 AICc=127.33 BIC=126.12
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 4098.811 7077.926 6008.525 18.28722 24.50654 0.8572381 -0.1974907
Forecasts:rownames(wpr_m014) <- "wpr_m014"
wpr_f014 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f014")
ARIMA(0,1,0) : 125.4245
ARIMA(0,1,0) with drift : 127.5424
ARIMA(0,1,1) : 130.1948
ARIMA(0,1,1) with drift : 137.4582
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 129.9333
ARIMA(1,1,0) with drift : 137.3915
ARIMA(1,1,1) : 139.3634
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 138.0696
ARIMA(2,1,0) with drift : 166.547
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 42550393: log likelihood=-61.21
AIC=124.42 AICc=125.42 BIC=124.22
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 3453.738 6039.186 5152.595 18.00039 24.46396 0.8572418 -0.2381519
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 42550393: log likelihood=-61.21
AIC=124.42 AICc=125.42 BIC=124.22
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 3453.738 6039.186 5152.595 18.00039 24.46396 0.8572418 -0.2381519
Forecasts:rownames(wpr_f014) <- "wpr_f014"
wpr_m15plus <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m15plus")
ARIMA(0,1,0) : 151.1578
ARIMA(0,1,0) with drift : 155.1099
ARIMA(0,1,1) : 156.1526
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 156.1532
ARIMA(1,1,0) with drift : 163.9579
ARIMA(1,1,1) : 166.1515
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 166.0983
ARIMA(2,1,0) with drift : 193.0796
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 3.101e+09: log likelihood=-74.08
AIC=150.16 AICc=151.16 BIC=149.95
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 19216.58 51559.25 39981.73 2.127952 4.821061 0.8594709 -0.2359366
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 3.101e+09: log likelihood=-74.08
AIC=150.16 AICc=151.16 BIC=149.95
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 19216.58 51559.25 39981.73 2.127952 4.821061 0.8594709 -0.2359366
Forecasts:rownames(wpr_m15plus) <- "wpr_m15plus"
wpr_f15plus <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f15plus")
ARIMA(0,1,0) : 143.6556
ARIMA(0,1,0) with drift : 146.9362
ARIMA(0,1,1) : 148.6553
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 148.6553
ARIMA(1,1,0) with drift : 154.5376
ARIMA(1,1,1) : 158.6553
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 158.6417
ARIMA(2,1,0) with drift : 182.9765
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 888234487: log likelihood=-70.33
AIC=142.66 AICc=143.66 BIC=142.45
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 12800.77 27592.46 20122.77 3.026658 5.073829 0.8592559 -0.3366906
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 888234487: log likelihood=-70.33
AIC=142.66 AICc=143.66 BIC=142.45
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 12800.77 27592.46 20122.77 3.026658 5.073829 0.8592559 -0.3366906
Forecasts:rownames(wpr_f15plus) <- "wpr_f15plus"
wpr_estimates <- rbind(wpr_m04, wpr_f04, wpr_m514, wpr_f514, wpr_m014, wpr_f014, wpr_m15plus, wpr_f15plus)
#EUR Estimates
eur_m04 <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_m04")
ARIMA(0,1,0) : 78.75381
ARIMA(0,1,0) with drift : 82.12951
ARIMA(0,1,1) : 83.64837
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : 93.2627
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 83.6904
ARIMA(1,1,0) with drift : 92.06954
ARIMA(1,1,1) : 93.5146
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : 123.2595
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : Inf
ARIMA(2,1,0) with drift : Inf
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 17815: log likelihood=-37.88
AIC=77.75 AICc=78.75 BIC=77.55
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -55.44929 123.5715 83.40786 -3.641441 5.183049 0.8598748 -0.1116328
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 17815: log likelihood=-37.88
AIC=77.75 AICc=78.75 BIC=77.55
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -55.44929 123.5715 83.40786 -3.641441 5.183049 0.8598748 -0.1116328
Forecasts:rownames(eur_m04) <- "eur_m04"
eur_f04 <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_f04")
ARIMA(0,1,0) : 76.68985
ARIMA(0,1,0) with drift : 80.33647
ARIMA(0,1,1) : 81.58578
ARIMA(0,1,1) with drift : 90.27588
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 81.57517
ARIMA(1,1,0) with drift : 90.28316
ARIMA(1,1,1) : 91.57496
ARIMA(1,1,1) with drift : 120.2759
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : Inf
ARIMA(2,1,0) with drift : 120.15
ARIMA(2,1,1) : 121.4982
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 12630: log likelihood=-36.84
AIC=75.69 AICc=76.69 BIC=75.48
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -43.06114 104.045 79.22457 -3.311926 5.619945 0.8595794 0.004291823
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 12630: log likelihood=-36.84
AIC=75.69 AICc=76.69 BIC=75.48
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -43.06114 104.045 79.22457 -3.311926 5.619945 0.8595794 0.004291823
Forecasts:rownames(eur_f04) <- "eur_f04"
eur_m514 <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_m514")
ARIMA(0,1,0) : 85.509
ARIMA(0,1,0) with drift : 86.43678
ARIMA(0,1,1) : Inf
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 90.11173
ARIMA(1,1,0) with drift : 95.54597
ARIMA(1,1,1) : Inf
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 100.0673
ARIMA(2,1,0) with drift : Inf
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 54922: log likelihood=-41.25
AIC=84.51 AICc=85.51 BIC=84.3
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -140.4223 216.9705 141.5777 -4.173495 4.202066 0.8606548 -0.4670638
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 54922: log likelihood=-41.25
AIC=84.51 AICc=85.51 BIC=84.3
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -140.4223 216.9705 141.5777 -4.173495 4.202066 0.8606548 -0.4670638
Forecasts:rownames(eur_m514) <- "eur_m514"
eur_f514 <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_f514")
ARIMA(0,1,0) : 84.80427
ARIMA(0,1,0) with drift : 86.44661
ARIMA(0,1,1) : 89.6705
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 89.57782
ARIMA(1,1,0) with drift : 94.21642
ARIMA(1,1,1) : Inf
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 99.14526
ARIMA(2,1,0) with drift : 121.6276
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 48836: log likelihood=-40.9
AIC=83.8 AICc=84.8 BIC=83.6
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -123.4541 204.5953 147.1173 -3.853962 4.643237 0.860335 -0.5094926
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 48836: log likelihood=-40.9
AIC=83.8 AICc=84.8 BIC=83.6
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -123.4541 204.5953 147.1173 -3.853962 4.643237 0.860335 -0.5094926
Forecasts:rownames(eur_f514) <- "eur_f514"
eur_m014 <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_m014")
ARIMA(0,1,0) : 87.90563
ARIMA(0,1,0) with drift : 87.17066
ARIMA(0,1,1) : Inf
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 91.3642
ARIMA(1,1,0) with drift : 96.87938
ARIMA(1,1,1) : Inf
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 101.202
ARIMA(2,1,0) with drift : 116.0782
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0) with drift
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
-224.5000
s.e. 72.4391
sigma^2 estimated as 37788: log likelihood=-39.59
AIC=83.17 AICc=87.17 BIC=82.75
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 0.8747853 164.2911 147.3034 0.003111067 2.892103 0.6561397 -0.1918559
Forecast method: ARIMA(0,1,0) with drift
Model Information:
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
-224.5000
s.e. 72.4391
sigma^2 estimated as 37788: log likelihood=-39.59
AIC=83.17 AICc=87.17 BIC=82.75
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 0.8747853 164.2911 147.3034 0.003111067 2.892103 0.6561397 -0.1918559
Forecasts:rownames(eur_m014) <- "eur_m014"
eur_f014 <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_f014")
ARIMA(0,1,0) : 87.89843
ARIMA(0,1,0) with drift : 89.26901
ARIMA(0,1,1) : 92.88049
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : 102.6458
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 92.86608
ARIMA(1,1,0) with drift : 97.28552
ARIMA(1,1,1) : Inf
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : 132.2977
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 102.1516
ARIMA(2,1,0) with drift : 126.8821
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 81791: log likelihood=-42.45
AIC=86.9 AICc=87.9 BIC=86.69
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -164.3724 264.7762 197.3419 -3.582997 4.247259 0.8605023 -0.5893928
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 81791: log likelihood=-42.45
AIC=86.9 AICc=87.9 BIC=86.69
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -164.3724 264.7762 197.3419 -3.582997 4.247259 0.8605023 -0.5893928
Forecasts:rownames(eur_f014) <- "eur_f014"
eur_m15plus <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_m15plus")
ARIMA(0,1,0) : 125.9029
ARIMA(0,1,0) with drift : 120.3483
ARIMA(0,1,1) : Inf
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : Inf
ARIMA(1,1,0) with drift : 128.3634
ARIMA(1,1,1) : Inf
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 129.0743
ARIMA(2,1,0) with drift : 155.9324
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0) with drift
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
-6176.333
s.e. 1150.038
sigma^2 estimated as 9528860: log likelihood=-56.17
AIC=116.35 AICc=120.35 BIC=115.93
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 25.60432 2608.894 2185.795 -0.08003539 1.376032 0.3538985 0.4138192
Forecast method: ARIMA(0,1,0) with drift
Model Information:
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
-6176.333
s.e. 1150.038
sigma^2 estimated as 9528860: log likelihood=-56.17
AIC=116.35 AICc=120.35 BIC=115.93
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 25.60432 2608.894 2185.795 -0.08003539 1.376032 0.3538985 0.4138192
Forecasts:rownames(eur_m15plus) <- "eur_m15plus"
eur_f15plus <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_f15plus")
ARIMA(0,1,0) : 116.7389
ARIMA(0,1,0) with drift : 104.0178
ARIMA(0,1,1) : Inf
ARIMA(0,1,1) with drift : 113.7773
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : Inf
ARIMA(1,1,0) with drift : 113.975
ARIMA(1,1,1) : Inf
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : 148.6417
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : Inf
ARIMA(2,1,0) with drift : 143.0256
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0) with drift
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
-3079.5000
s.e. 294.9107
sigma^2 estimated as 627839: log likelihood=-48.01
AIC=100.02 AICc=104.02 BIC=99.6
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 12.97978 669.6689 474.9798 -0.01880938 0.588106 0.1542393 0.0977167
Forecast method: ARIMA(0,1,0) with drift
Model Information:
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
-3079.5000
s.e. 294.9107
sigma^2 estimated as 627839: log likelihood=-48.01
AIC=100.02 AICc=104.02 BIC=99.6
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 12.97978 669.6689 474.9798 -0.01880938 0.588106 0.1542393 0.0977167
Forecasts:rownames(eur_f15plus) <- "eur_f15plus"
eur_estimates <- rbind(eur_m04, eur_f04, eur_m514, eur_f514, eur_m014, eur_f014, eur_m15plus, eur_f15plus)
#AMR Estimates
emr_m04 <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_m04")
ARIMA(0,1,0) : 110.5715
ARIMA(0,1,0) with drift : 106.0002
ARIMA(0,1,1) : 111.9022
ARIMA(0,1,1) with drift : 115.5503
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 107.6886
ARIMA(1,1,0) with drift : 115.508
ARIMA(1,1,1) : 117.6708
ARIMA(1,1,1) with drift : 145.4466
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 117.6505
ARIMA(2,1,0) with drift : 145.242
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0) with drift
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
1689.1667
s.e. 347.8854
sigma^2 estimated as 871360: log likelihood=-49
AIC=102 AICc=106 BIC=101.58
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 0.532833 788.9232 524.0566 0.6179599 3.986697 0.3102457 0.2009148
Forecast method: ARIMA(0,1,0) with drift
Model Information:
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
1689.1667
s.e. 347.8854
sigma^2 estimated as 871360: log likelihood=-49
AIC=102 AICc=106 BIC=101.58
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 0.532833 788.9232 524.0566 0.6179599 3.986697 0.3102457 0.2009148
Forecasts:rownames(emr_m04) <- "emr_m04"
emr_f04 <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_f04")
ARIMA(0,1,0) : 107.6113
ARIMA(0,1,0) with drift : 103.7642
ARIMA(0,1,1) : 110.6912
ARIMA(0,1,1) with drift : 113.6269
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 107.7608
ARIMA(1,1,0) with drift : 113.5916
ARIMA(1,1,1) : 117.0607
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 116.1464
ARIMA(2,1,0) with drift : 143.3858
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0) with drift
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
1298.1667
s.e. 288.7444
sigma^2 estimated as 600277: log likelihood=-47.88
AIC=99.76 AICc=103.76 BIC=99.35
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 0.4144046 654.805 447.462 0.7599193 4.915919 0.3446877 -0.1203015
Forecast method: ARIMA(0,1,0) with drift
Model Information:
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
1298.1667
s.e. 288.7444
sigma^2 estimated as 600277: log likelihood=-47.88
AIC=99.76 AICc=103.76 BIC=99.35
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 0.4144046 654.805 447.462 0.7599193 4.915919 0.3446877 -0.1203015
Forecasts:rownames(emr_f04) <- "emr_f04"
emr_m514 <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_m514")
ARIMA(0,1,0) : 106.2175
ARIMA(0,1,0) with drift : 106.5495
ARIMA(0,1,1) : Inf
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 108.4115
ARIMA(1,1,0) with drift : 116.5418
ARIMA(1,1,1) : Inf
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 117.5946
ARIMA(2,1,0) with drift : 144.3844
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 1732475: log likelihood=-51.61
AIC=105.22 AICc=106.22 BIC=105.01
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 831.4019 1218.597 893.9733 5.034561 5.531396 0.8589015 0.2435649
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 1732475: log likelihood=-51.61
AIC=105.22 AICc=106.22 BIC=105.01
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 831.4019 1218.597 893.9733 5.034561 5.531396 0.8589015 0.2435649
Forecasts:rownames(emr_m514) <- "emr_m514"
emr_f514 <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_f514")
ARIMA(0,1,0) : 106.7125
ARIMA(0,1,0) with drift : 109.5098
ARIMA(0,1,1) : Inf
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 110.6453
ARIMA(1,1,0) with drift : 119.4974
ARIMA(1,1,1) : Inf
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 119.929
ARIMA(2,1,0) with drift : 148.2944
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 1881467: log likelihood=-51.86
AIC=105.71 AICc=106.71 BIC=105.5
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 654.1529 1269.916 998.7243 3.163926 5.159425 0.8592409 0.1308076
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 1881467: log likelihood=-51.86
AIC=105.71 AICc=106.71 BIC=105.5
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 654.1529 1269.916 998.7243 3.163926 5.159425 0.8592409 0.1308076
Forecasts:rownames(emr_f514) <- "emr_f514"
emr_m014 <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_m014")
ARIMA(0,1,0) : 117.0617
ARIMA(0,1,0) with drift : 115.5705
ARIMA(0,1,1) : 118.5877
ARIMA(0,1,1) with drift : 125.0624
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 116.8657
ARIMA(1,1,0) with drift : 125.2341
ARIMA(1,1,1) : 126.7096
ARIMA(1,1,1) with drift : 155.0521
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 126.5296
ARIMA(2,1,0) with drift : 154.4364
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0) with drift
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
2641.8333
s.e. 772.3193
sigma^2 estimated as 4294599: log likelihood=-53.79
AIC=111.57 AICc=115.57 BIC=111.15
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 2.240166 1751.448 1276.383 0.1088444 4.518639 0.4637739 0.1764573
Forecast method: ARIMA(0,1,0) with drift
Model Information:
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
2641.8333
s.e. 772.3193
sigma^2 estimated as 4294599: log likelihood=-53.79
AIC=111.57 AICc=115.57 BIC=111.15
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 2.240166 1751.448 1276.383 0.1088444 4.518639 0.4637739 0.1764573
Forecasts:rownames(emr_m014) <- "emr_m014"
emr_f014 <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_f014")
ARIMA(0,1,0) : 114.8804
ARIMA(0,1,0) with drift : 114.8509
ARIMA(0,1,1) : 116.6524
ARIMA(0,1,1) with drift : 124.3216
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 115.7161
ARIMA(1,1,0) with drift : 124.5359
ARIMA(1,1,1) : 125.4571
ARIMA(1,1,1) with drift : 154.3157
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 125.0684
ARIMA(2,1,0) with drift : 153.5805
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0) with drift
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
2041.0000
s.e. 727.3703
sigma^2 estimated as 3809285: log likelihood=-53.43
AIC=110.85 AICc=114.85 BIC=110.43
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 2.761856 1649.52 1225.619 0.05313471 4.220174 0.5291965 0.162671
Forecast method: ARIMA(0,1,0) with drift
Model Information:
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
2041.0000
s.e. 727.3703
sigma^2 estimated as 3809285: log likelihood=-53.43
AIC=110.85 AICc=114.85 BIC=110.43
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 2.761856 1649.52 1225.619 0.05313471 4.220174 0.5291965 0.162671
Forecasts:rownames(emr_f014) <- "emr_f014"
emr_m15plus <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_m15plus")
ARIMA(0,1,0) : 133.2224
ARIMA(0,1,0) with drift : 136.4537
ARIMA(0,1,1) : 138.0252
ARIMA(0,1,1) with drift : 146.4274
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : 176.2163
ARIMA(1,1,0) : 137.8248
ARIMA(1,1,0) with drift : 146.4201
ARIMA(1,1,1) : 147.6019
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 147.0673
ARIMA(2,1,0) with drift : 176.3108
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 156081396: log likelihood=-65.11
AIC=132.22 AICc=133.22 BIC=132.01
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 5437.489 11566.51 8570.917 2.486889 3.87156 0.8598432 -0.120371
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 156081396: log likelihood=-65.11
AIC=132.22 AICc=133.22 BIC=132.01
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 5437.489 11566.51 8570.917 2.486889 3.87156 0.8598432 -0.120371
Forecasts:rownames(emr_m15plus) <- "emr_m15plus"
emr_f15plus <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_f15plus")
ARIMA(0,1,0) : 131.2118
ARIMA(0,1,0) with drift : 135.5512
ARIMA(0,1,1) : 136.2115
ARIMA(0,1,1) with drift : 145.4442
ARIMA(0,1,2) : 145.3951
ARIMA(0,1,2) with drift : 175.3264
ARIMA(1,1,0) : 136.2112
ARIMA(1,1,0) with drift : 145.4197
ARIMA(1,1,1) : 146.1754
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 145.6273
ARIMA(2,1,0) with drift : 175.2787
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 111641770: log likelihood=-64.11
AIC=130.21 AICc=131.21 BIC=130
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 2949.988 9782.277 7055.131 1.426539 3.469009 0.8602944 -0.1560189
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 111641770: log likelihood=-64.11
AIC=130.21 AICc=131.21 BIC=130
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 2949.988 9782.277 7055.131 1.426539 3.469009 0.8602944 -0.1560189
Forecasts:rownames(emr_f15plus) <- "emr_f15plus"
emr_estimates <- rbind(emr_m04, emr_f04, emr_m514, emr_f514, emr_m014, emr_f014, emr_m15plus, emr_f15plus)
#WPR Estimates
wpr_m04 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m04")
ARIMA(0,1,0) : 117.987
ARIMA(0,1,0) with drift : 120.6596
ARIMA(0,1,1) : 122.6039
ARIMA(0,1,1) with drift : 130.6566
ARIMA(0,1,2) : 132.1259
ARIMA(0,1,2) with drift : 160.3447
ARIMA(1,1,0) : 122.4011
ARIMA(1,1,0) with drift : 130.6558
ARIMA(1,1,1) : 132.1779
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : 161.9353
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 131.972
ARIMA(2,1,0) with drift : 160.5433
ARIMA(2,1,1) : 161.9623
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 12318696: log likelihood=-57.49
AIC=116.99 AICc=117.99 BIC=116.78
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 1705.945 3249.443 2805.374 20.95097 30.38595 0.8571698 -0.1090836
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 12318696: log likelihood=-57.49
AIC=116.99 AICc=117.99 BIC=116.78
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 1705.945 3249.443 2805.374 20.95097 30.38595 0.8571698 -0.1090836
Forecasts:rownames(wpr_m04) <- "wpr_m04"
wpr_f04 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f04")
ARIMA(0,1,0) : 115.7528
ARIMA(0,1,0) with drift : 118.5332
ARIMA(0,1,1) : 120.4465
ARIMA(0,1,1) with drift : 128.5172
ARIMA(0,1,2) : 130.0029
ARIMA(0,1,2) with drift : 158.2683
ARIMA(1,1,0) : 120.2961
ARIMA(1,1,0) with drift : 128.5132
ARIMA(1,1,1) : 130.083
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : 159.8347
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 129.8787
ARIMA(2,1,0) with drift : 158.4132
ARIMA(2,1,1) : 159.8769
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 8488853: log likelihood=-56.38
AIC=114.75 AICc=115.75 BIC=114.54
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 1388.774 2697.436 2351.631 21.47588 31.76637 0.8571647 -0.1220096
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 8488853: log likelihood=-56.38
AIC=114.75 AICc=115.75 BIC=114.54
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 1388.774 2697.436 2351.631 21.47588 31.76637 0.8571647 -0.1220096
Forecasts:rownames(wpr_f04) <- "wpr_f04"
wpr_m514 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m514")
ARIMA(0,1,0) : 121.504
ARIMA(0,1,0) with drift : 123.8299
ARIMA(0,1,1) : 126.3435
ARIMA(0,1,1) with drift : 133.7516
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 126.0875
ARIMA(1,1,0) with drift : 133.6501
ARIMA(1,1,1) : 135.4448
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : Inf
ARIMA(2,1,0) with drift : Inf
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 22137609: log likelihood=-59.25
AIC=120.5 AICc=121.5 BIC=120.3
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 2418.811 4356.041 3571.382 17.62742 23.71631 0.8572347 -0.263211
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 22137609: log likelihood=-59.25
AIC=120.5 AICc=121.5 BIC=120.3
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 2418.811 4356.041 3571.382 17.62742 23.71631 0.8572347 -0.263211
Forecasts:rownames(wpr_m514) <- "wpr_m514"
wpr_f514 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f514")
ARIMA(0,1,0) : 119.8234
ARIMA(0,1,0) with drift : 122.1114
ARIMA(0,1,1) : 124.7307
ARIMA(0,1,1) with drift : 131.9174
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 124.5719
ARIMA(1,1,0) with drift : 131.6532
ARIMA(1,1,1) : 133.8761
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : Inf
ARIMA(2,1,0) with drift : Inf
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 16729524: log likelihood=-58.41
AIC=118.82 AICc=119.82 BIC=118.62
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 2114.529 3786.765 3049.1 17.08469 22.6313 0.8572513 -0.3350534
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 16729524: log likelihood=-58.41
AIC=118.82 AICc=119.82 BIC=118.62
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 2114.529 3786.765 3049.1 17.08469 22.6313 0.8572513 -0.3350534
Forecasts:rownames(wpr_f514) <- "wpr_f514"
wpr_m014 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m014")
ARIMA(0,1,0) : 127.329
ARIMA(0,1,0) with drift : 129.3522
ARIMA(0,1,1) : 132.0091
ARIMA(0,1,1) with drift : 139.3173
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 131.6732
ARIMA(1,1,0) with drift : 139.2927
ARIMA(1,1,1) : 141.1479
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 140.1957
ARIMA(2,1,0) with drift : 168.6569
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 58446545: log likelihood=-62.16
AIC=126.33 AICc=127.33 BIC=126.12
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 4098.811 7077.926 6008.525 18.28722 24.50654 0.8572381 -0.1974907
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 58446545: log likelihood=-62.16
AIC=126.33 AICc=127.33 BIC=126.12
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 4098.811 7077.926 6008.525 18.28722 24.50654 0.8572381 -0.1974907
Forecasts:rownames(wpr_m014) <- "wpr_m014"
wpr_f014 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f014")
ARIMA(0,1,0) : 125.4245
ARIMA(0,1,0) with drift : 127.5424
ARIMA(0,1,1) : 130.1948
ARIMA(0,1,1) with drift : 137.4582
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 129.9333
ARIMA(1,1,0) with drift : 137.3915
ARIMA(1,1,1) : 139.3634
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 138.0696
ARIMA(2,1,0) with drift : 166.547
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 42550393: log likelihood=-61.21
AIC=124.42 AICc=125.42 BIC=124.22
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 3453.738 6039.186 5152.595 18.00039 24.46396 0.8572418 -0.2381519
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 42550393: log likelihood=-61.21
AIC=124.42 AICc=125.42 BIC=124.22
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 3453.738 6039.186 5152.595 18.00039 24.46396 0.8572418 -0.2381519
Forecasts:rownames(wpr_f014) <- "wpr_f014"
wpr_m15plus <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m15plus")
ARIMA(0,1,0) : 151.1578
ARIMA(0,1,0) with drift : 155.1099
ARIMA(0,1,1) : 156.1526
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 156.1532
ARIMA(1,1,0) with drift : 163.9579
ARIMA(1,1,1) : 166.1515
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 166.0983
ARIMA(2,1,0) with drift : 193.0796
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 3.101e+09: log likelihood=-74.08
AIC=150.16 AICc=151.16 BIC=149.95
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 19216.58 51559.25 39981.73 2.127952 4.821061 0.8594709 -0.2359366
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 3.101e+09: log likelihood=-74.08
AIC=150.16 AICc=151.16 BIC=149.95
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 19216.58 51559.25 39981.73 2.127952 4.821061 0.8594709 -0.2359366
Forecasts:rownames(wpr_m15plus) <- "wpr_m15plus"
wpr_f15plus <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f15plus")
ARIMA(0,1,0) : 143.6556
ARIMA(0,1,0) with drift : 146.9362
ARIMA(0,1,1) : 148.6553
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 148.6553
ARIMA(1,1,0) with drift : 154.5376
ARIMA(1,1,1) : 158.6553
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 158.6417
ARIMA(2,1,0) with drift : 182.9765
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 888234487: log likelihood=-70.33
AIC=142.66 AICc=143.66 BIC=142.45
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 12800.77 27592.46 20122.77 3.026658 5.073829 0.8592559 -0.3366906
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 888234487: log likelihood=-70.33
AIC=142.66 AICc=143.66 BIC=142.45
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 12800.77 27592.46 20122.77 3.026658 5.073829 0.8592559 -0.3366906
Forecasts:rownames(wpr_f15plus) <- "wpr_f15plus"
wpr_estimates <- rbind(wpr_m04, wpr_f04, wpr_m514, wpr_f514, wpr_m014, wpr_f014, wpr_m15plus, wpr_f15plus)
#AMR Estimates
amr_m04 <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_m04")
ARIMA(0,1,0) : 81.20312
ARIMA(0,1,0) with drift : 85.62773
ARIMA(0,1,1) : Inf
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 83.36317
ARIMA(1,1,0) with drift : 93.10285
ARIMA(1,1,1) : Inf
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 91.99538
ARIMA(2,1,0) with drift : 121.9132
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 26796: log likelihood=-39.1
AIC=80.2 AICc=81.2 BIC=79.99
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -42.09157 151.5522 120.1941 -2.150665 5.588892 0.8595529 -0.4510363
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 26796: log likelihood=-39.1
AIC=80.2 AICc=81.2 BIC=79.99
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -42.09157 151.5522 120.1941 -2.150665 5.588892 0.8595529 -0.4510363
Forecasts:rownames(amr_m04) <- "amr_m04"
amr_f04 <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_f04")
ARIMA(0,1,0) : 79.78439
ARIMA(0,1,0) with drift : 84.32867
ARIMA(0,1,1) : 81.41097
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 80.0876
ARIMA(1,1,0) with drift : 89.04987
ARIMA(1,1,1) : 89.05019
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 88.27209
ARIMA(2,1,0) with drift : 116.0278
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 21153: log likelihood=-38.39
AIC=78.78 AICc=79.78 BIC=78.58
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -33.42686 134.653 118.8589 -2.025789 6.38553 0.8592207 -0.7441053
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 21153: log likelihood=-38.39
AIC=78.78 AICc=79.78 BIC=78.58
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -33.42686 134.653 118.8589 -2.025789 6.38553 0.8592207 -0.7441053
Forecasts:rownames(amr_f04) <- "amr_f04"
amr_m514 <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_m514")
ARIMA(0,1,0) : 80.43822
ARIMA(0,1,0) with drift : 82.89086
ARIMA(0,1,1) : 85.35448
ARIMA(0,1,1) with drift : 92.87508
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 85.25797
ARIMA(1,1,0) with drift : 92.87029
ARIMA(1,1,1) : 94.93816
ARIMA(1,1,1) with drift : 122.6887
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 94.65097
ARIMA(2,1,0) with drift : 122.7689
ARIMA(2,1,1) : 124.598
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 23590: log likelihood=-38.72
AIC=79.44 AICc=80.44 BIC=79.23
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -76.94743 142.1967 104.7669 -2.600088 3.547003 0.8610975 -0.2447804
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 23590: log likelihood=-38.72
AIC=79.44 AICc=80.44 BIC=79.23
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -76.94743 142.1967 104.7669 -2.600088 3.547003 0.8610975 -0.2447804
Forecasts:rownames(amr_m514) <- "amr_m514"
amr_f514 <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_f514")
ARIMA(0,1,0) : 81.77671
ARIMA(0,1,0) with drift : 85.53773
ARIMA(0,1,1) : 85.39397
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 84.22718
ARIMA(1,1,0) with drift : 92.24655
ARIMA(1,1,1) : 93.97957
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : Inf
ARIMA(2,1,0) with drift : Inf
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 29485: log likelihood=-39.39
AIC=80.78 AICc=81.78 BIC=80.57
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -63.094 158.9745 120.906 -2.160652 3.97228 0.8605409 -0.510281
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 29485: log likelihood=-39.39
AIC=80.78 AICc=81.78 BIC=80.57
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -63.094 158.9745 120.906 -2.160652 3.97228 0.8605409 -0.510281
Forecasts:rownames(amr_f514) <- "amr_f514"
amr_m014 <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_m014")
ARIMA(0,1,0) : 84.45934
ARIMA(0,1,0) with drift : 87.54733
ARIMA(0,1,1) : 89.18806
ARIMA(0,1,1) with drift : 97.48774
ARIMA(0,1,2) : 99.10081
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 89.11319
ARIMA(1,1,0) with drift : 97.52031
ARIMA(1,1,1) : 99.10554
ARIMA(1,1,1) with drift : 127.4837
ARIMA(1,1,2) : 129.0897
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : Inf
ARIMA(2,1,0) with drift : 127.3181
ARIMA(2,1,1) : 129.1039
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 46111: log likelihood=-40.73
AIC=83.46 AICc=84.46 BIC=83.25
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -95.30943 198.8054 97.83343 -1.802642 1.847534 0.8645075 -0.1921184
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 46111: log likelihood=-40.73
AIC=83.46 AICc=84.46 BIC=83.25
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -95.30943 198.8054 97.83343 -1.802642 1.847534 0.8645075 -0.1921184
Forecasts:rownames(amr_m014) <- "amr_m014"
amr_f014 <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_f014")
ARIMA(0,1,0) : 81.98907
ARIMA(0,1,0) with drift : 85.52465
ARIMA(0,1,1) : 86.57645
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 86.05811
ARIMA(1,1,0) with drift : 93.61714
ARIMA(1,1,1) : 95.78387
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 95.5139
ARIMA(2,1,0) with drift : 123.5324
ARIMA(2,1,1) : 125.511
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 30550: log likelihood=-39.49
AIC=80.99 AICc=81.99 BIC=80.78
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -68.933 161.8208 117.067 -1.369866 2.307828 0.8629017 -0.5168809
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 30550: log likelihood=-39.49
AIC=80.99 AICc=81.99 BIC=80.78
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set -68.933 161.8208 117.067 -1.369866 2.307828 0.8629017 -0.5168809
Forecasts:rownames(amr_f014) <- "amr_f014"
amr_m15plus <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_m15plus")
ARIMA(0,1,0) : 121.5033
ARIMA(0,1,0) with drift : 121.2176
ARIMA(0,1,1) : 124.9374
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : 133.9693
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 124.2796
ARIMA(1,1,0) with drift : 130.5986
ARIMA(1,1,1) : 134.1506
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : 163.6115
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 134.0446
ARIMA(2,1,0) with drift : 160.3891
ARIMA(2,1,1) : 164.0426
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0) with drift
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
3600.333
s.e. 1236.437
sigma^2 estimated as 11010114: log likelihood=-56.61
AIC=117.22 AICc=121.22 BIC=116.8
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 17.92894 2804.348 2433.643 -0.0223538 1.731512 0.6560275 -0.2334609
Forecast method: ARIMA(0,1,0) with drift
Model Information:
Series: .
ARIMA(0,1,0) with drift
Coefficients:
drift
3600.333
s.e. 1236.437
sigma^2 estimated as 11010114: log likelihood=-56.61
AIC=117.22 AICc=121.22 BIC=116.8
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 17.92894 2804.348 2433.643 -0.0223538 1.731512 0.6560275 -0.2334609
Forecasts:rownames(amr_m15plus) <- "amr_m15plus"
amr_f15plus <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_f15plus")
ARIMA(0,1,0) : 107.077
ARIMA(0,1,0) with drift : 111.465
ARIMA(0,1,1) : 111.6485
ARIMA(0,1,1) with drift : Inf
ARIMA(0,1,2) : Inf
ARIMA(0,1,2) with drift : Inf
ARIMA(1,1,0) : 111.8425
ARIMA(1,1,0) with drift : 120.2414
ARIMA(1,1,1) : 121.6324
ARIMA(1,1,1) with drift : Inf
ARIMA(1,1,2) : Inf
ARIMA(1,1,2) with drift : Inf
ARIMA(2,1,0) : 120.2146
ARIMA(2,1,0) with drift : 144.5938
ARIMA(2,1,1) : Inf
ARIMA(2,1,1) with drift : Inf
ARIMA(2,1,2) : Inf
ARIMA(2,1,2) with drift : Inf
Best model: ARIMA(0,1,0)
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 2e+06: log likelihood=-52.04
AIC=106.08 AICc=107.08 BIC=105.87
Training set error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 388.1107 1309.374 1138.968 0.4951078 1.50792 0.8652579 -0.2900684
Forecast method: ARIMA(0,1,0)
Model Information:
Series: .
ARIMA(0,1,0)
sigma^2 estimated as 2e+06: log likelihood=-52.04
AIC=106.08 AICc=107.08 BIC=105.87
Error measures:
ME RMSE MAE MPE MAPE MASE ACF1
Training set 388.1107 1309.374 1138.968 0.4951078 1.50792 0.8652579 -0.2900684
Forecasts:rownames(amr_f15plus) <- "amr_f15plus"
amr_estimates <- rbind(amr_m04, amr_f04, amr_m514, amr_f514, amr_m014, amr_f014, amr_m15plus, amr_f15plus)
Combining the data for 2020 with the model estimates
##AFR difference calculations
afr_2020 <- tot_world %>% filter(year == 2020) %>% filter(g_whoregion == "AFR") %>% t()
afr_2020 <- as.data.frame(afr_2020[c(3:10), ])
rownames(afr_2020) <- c("afr_m04", "afr_f04", "afr_m514", "afr_f514", "afr_m014", "afr_f014", "afr_m15plus", "afr_f15plus")
colnames(afr_2020) <- "notif_2020"
est_afr_2020 <- cbind(afr_2020, afr_estimates)
colnames(est_afr_2020) <- c("notif_2020", "est_2020", "Lo_80", "Hi_80", "Lo_95", "Hi_95")
est_afr_2020$notif_2020 <- as.numeric(est_afr_2020$notif_2020)
dif_afr <- mutate(est_afr_2020, "Difference" = notif_2020 - est_2020, "afr_perc" = 100*(Difference/est_2020))
##SEA difference calculations
sea_2020 <- tot_world %>% filter(year == 2020) %>% filter(g_whoregion == "SEA") %>% t()
sea_2020 <- as.data.frame(sea_2020[c(3:10), ])
rownames(sea_2020) <- c("sea_m04", "sea_f04", "sea_m514", "sea_f514", "sea_m014", "sea_f014", "sea_m15plus", "sea_f15plus")
colnames(sea_2020) <- "notif_2020"
est_sea_2020 <- cbind(sea_2020, sea_estimates)
colnames(est_sea_2020) <- c("notif_2020", "est_2020", "Lo_80", "Hi_80", "Lo_95", "Hi_95")
est_sea_2020$notif_2020 <- as.numeric(est_sea_2020$notif_2020)
dif_sea <- mutate(est_sea_2020, "Difference" = notif_2020 - est_2020, "sea_perc" = 100*(Difference/est_2020))
#WPR difference calculations
wpr_2020 <- tot_world %>% filter(year == 2020) %>% filter(g_whoregion == "WPR") %>% t()
wpr_2020 <- as.data.frame(wpr_2020[c(3:10), ])
rownames(wpr_2020) <- c("wpr_m04", "wpr_f04", "wpr_m514", "wpr_f514", "wpr_m014", "wpr_f014", "wpr_m15plus", "wpr_f15plus")
colnames(wpr_2020) <- "wpr_2020"
est_wpr_2020 <- cbind(wpr_2020, wpr_estimates)
colnames(est_wpr_2020) <- c("notif_2020", "est_2020", "Lo_80", "Hi_80", "Lo_95", "Hi_95")
est_wpr_2020$notif_2020 <- as.numeric(est_wpr_2020$notif_2020)
dif_wpr <- mutate(est_wpr_2020, "Difference" = notif_2020 - est_2020, "wpr_perc" = 100*(Difference/est_2020))
#WPR difference calculations
eur_2020 <- tot_world %>% filter(year == 2020) %>% filter(g_whoregion == "EUR") %>% t()
eur_2020 <- as.data.frame(eur_2020[c(3:10), ])
rownames(eur_2020) <- c("eur_m04", "eur_f04", "eur_m514", "eur_f514", "eur_m014", "eur_f014", "eur_m15plus", "eur_f15plus")
colnames(eur_2020) <- "eur_2020"
est_eur_2020 <- cbind(eur_2020, eur_estimates)
colnames(est_eur_2020) <- c("notif_2020", "est_2020", "Lo_80", "Hi_80", "Lo_95", "Hi_95")
est_eur_2020$notif_2020 <- as.numeric(est_eur_2020$notif_2020)
dif_eur <- mutate(est_eur_2020, "Difference" = notif_2020 - est_2020, "eur_perc" = 100*(Difference/est_2020))
#AMR difference calculations
amr_2020 <- tot_world %>% filter(year == 2020) %>% filter(g_whoregion == "AMR") %>% t()
amr_2020 <- as.data.frame(amr_2020[c(3:10), ])
rownames(amr_2020) <- c("amr_m04", "amr_f04", "amr_m514", "amr_f514", "amr_m014", "amr_f014", "amr_m15plus", "amr_f15plus")
colnames(eur_2020) <- "amr_2020"
est_amr_2020 <- cbind(amr_2020, amr_estimates)
colnames(est_amr_2020) <- c("notif_2020", "est_2020", "Lo_80", "Hi_80", "Lo_95", "Hi_95")
est_amr_2020$notif_2020 <- as.numeric(est_amr_2020$notif_2020)
dif_amr <- mutate(est_amr_2020, "Difference" = notif_2020 - est_2020, "amr_perc" = 100*(Difference/est_2020))
#EMR difference calculations
emr_2020 <- tot_world %>% filter(year == 2020) %>% filter(g_whoregion == "EMR") %>% t()
emr_2020 <- as.data.frame(emr_2020[c(3:10), ])
rownames(emr_2020) <- c("emr_m04", "emr_f04", "emr_m514", "emr_f514", "emr_m014", "emr_f014", "emr_m15plus", "emr_f15plus")
colnames(emr_2020) <- "emr_2020"
est_emr_2020 <- cbind(emr_2020, emr_estimates)
colnames(est_emr_2020) <- c("notif_2020", "est_2020", "Lo_80", "Hi_80", "Lo_95", "Hi_95")
est_emr_2020$notif_2020 <- as.numeric(est_emr_2020$notif_2020)
dif_emr <- mutate(est_emr_2020, "Difference" = notif_2020 - est_2020, "emr_perc" = 100*(Difference/est_2020))
#Combined data frame with difference data
combined_dif <- data.frame(dif_afr[, c(7,8)], dif_amr[,c(7,8)], dif_emr[, c(7,8)], dif_eur[,c(7:8)], dif_sea[,c(7:8)], dif_wpr[,c(7:8)])
combined_dif$group <- as.vector(rownames(combined_dif))
combined_dif$group <- c("m04", "f04", "m514", "f514", "m014", "f014", "m15plus", "f15plus")
rownames(combined_dif) <- c(1:8)
combined_dif <- combined_dif[, c(13,1:12)]
colnames(combined_dif) <- c("group", "AFR_dif", "AFR_perc", "AMR_dif", "AMR_perc", "EMR_dif", "EMR_perc", "EUR_dif", "EUR_perc", "SEA_dif", "SEA_perc", "WPR_dif", "WPR_perc")
#isolating only the percentage data to be able to plot a bar plot
bar_data <- combined_dif[, c(1,3,5,7,9,11,13)]
bar_newgroup <- str_split_fixed(bar_data$group, "", 2)
bar_data2 <- cbind(bar_data, bar_newgroup)
colnames(bar_data2) <- c("group", "AFR", "AMR", "EMR", "EUR", "SEA", "WPR", "sex", "age_group")
bar_data2 <- bar_data2[, c(9,8,2:7)]
piv_dif <- pivot_longer(bar_data2, cols = c("AFR", "AMR", "EMR", "EUR", "SEA", "WPR"), names_to = "g_whoregion", values_to = "value")
library(dplyr)
library(ggplot2)
piv_three <- filter(piv_dif, age_group != "014")
##Attempt at drawing plots including 0-14
arima_dif <- ggplot(piv_dif, aes(x = g_whoregion, y = value, fill = sex)) + facet_wrap(~piv_dif$age_group) + geom_bar(stat = "identity", position = "dodge")
##Attempt excluding 0-14
piv_copy <- piv_three
piv_copy$age_group <- factor(piv_copy$age_group, levels = c("04", "514", "15plus"))
arima_copy <- ggplot(piv_copy, aes(x = g_whoregion, y = value, fill = sex)) + facet_wrap(~piv_copy$age_group) + geom_bar(stat = "identity", position = "dodge")
arima_copy
arima_three <- ggplot(piv_three, aes(x = g_whoregion, y = value, fill = sex)) + facet_wrap(~piv_three$age_group) + geom_bar(stat = "identity", position = "dodge")
arima_three
NA#Attempt at the forecasting models
#Rearranging data for 0-4 males
males_04 <- select(perc_world, c(g_whoregion, year, newrel_m04))
piv_na <- pivot_wider(males_04, names_from = "year", values_from = "newrel_m04")
piv_na$`2020` <- NA
males_back <- pivot_longer(piv_na, cols = c(as.vector(colnames(piv_na[,2:9]))), names_to = "year")
males_back$year <- as.numeric(males_back$year)
males_no_pivot <- males_04
males_pivot <- pivot_wider(males_04, names_from = "g_whoregion", values_from = "newrel_m04")
select(afr_m04, "Point Forecast")
fore2020 <- function(df){
a <- select(df, "Point Forecast")
b <- "2020"
colnames(a) <- "Number"
rownames(a) <- NULL
a$Year <- b
return(a)
}
#males 04
fore_afrm04 <- fore2020(afr_m04)
fore_amrm04 <- fore2020(amr_m04)
fore_emrm04 <- fore2020(emr_m04)
fore_eurm04 <- fore2020(eur_m04)
fore_seam04 <- fore2020(sea_m04)
fore_wprm04 <- fore2020(wpr_m04)
bound_fore <- cbind(fore_afrm04, fore_amrm04$Number, fore_emrm04$Number, fore_eurm04$Number, fore_seam04$Number, fore_wprm04$Number)
colnames(bound_fore) <- c("AFR", "Year", "AMR", "EMR", "EUR", "SEA", "WPR")
bound_fore <- bound_fore[, c(2, 1, 3:7)]
#females04
fore_afrf04 <- fore2020(afr_f04)
fore_amrf04 <- fore2020(amr_f04)
fore_emrf04 <- fore2020(emr_f04)
fore_eurf04 <- fore2020(eur_f04)
fore_seaf04 <- fore2020(sea_f04)
fore_wprf04 <- fore2020(wpr_f04)
bound_f04 <- cbind(fore_afrf04, fore_amrf04$Number, fore_emrf04$Number, fore_eurf04$Number, fore_seaf04$Number, fore_wprf04$Number)
colnames(bound_f04) <- c("AFR", "Year", "AMR", "EMR", "EUR", "SEA", "WPR")
bound_f04 <- bound_f04[, c(2, 1, 3:7)]
#males 514
fore_afrm514 <- fore2020(afr_m514)
fore_amrm514 <- fore2020(amr_m514)
fore_emrm514 <- fore2020(emr_m514)
fore_eurm514 <- fore2020(eur_m514)
fore_seam514 <- fore2020(sea_m514)
fore_wprm514 <- fore2020(wpr_m514)
bound_m514 <- cbind(fore_afrm514, fore_amrm514$Number, fore_emrm514$Number, fore_eurm514$Number, fore_seam514$Number, fore_wprm514$Number)
colnames(bound_m514) <- c("AFR", "Year", "AMR", "EMR", "EUR", "SEA", "WPR")
bound_m514 <- bound_m514[, c(2, 1, 3:7)]
#females 514
fore_afrf514 <- fore2020(afr_f514)
fore_amrf514 <- fore2020(amr_f514)
fore_emrf514 <- fore2020(emr_f514)
fore_eurf514 <- fore2020(eur_f514)
fore_seaf514 <- fore2020(sea_f514)
fore_wprf514 <- fore2020(wpr_f514)
bound_f514 <- cbind(fore_afrf514, fore_amrf514$Number, fore_emrf514$Number, fore_eurf514$Number, fore_seaf514$Number, fore_wprf514$Number)
colnames(bound_f514) <- c("AFR", "Year", "AMR", "EMR", "EUR", "SEA", "WPR")
bound_f514 <- bound_f514[, c(2, 1, 3:7)]
#males 15plus
fore_afrm15plus <- fore2020(afr_m15plus)
fore_amrm15plus <- fore2020(amr_m15plus)
fore_emrm15plus <- fore2020(emr_m15plus)
fore_eurm15plus <- fore2020(eur_m15plus)
fore_seam15plus <- fore2020(sea_m15plus)
fore_wprm15plus <- fore2020(wpr_m15plus)
bound_m15plus <- cbind(fore_afrm15plus, fore_amrm15plus$Number, fore_emrm15plus$Number, fore_eurm15plus$Number, fore_seam15plus$Number, fore_wprm15plus$Number)
colnames(bound_m15plus) <- c("AFR", "Year", "AMR", "EMR", "EUR", "SEA", "WPR")
bound_m15plus <- bound_m15plus[, c(2, 1, 3:7)]
#females 15plus
fore_afrf15plus <- fore2020(afr_f15plus)
fore_amrf15plus <- fore2020(amr_f15plus)
fore_emrf15plus <- fore2020(emr_f15plus)
fore_eurf15plus <- fore2020(eur_f15plus)
fore_seaf15plus <- fore2020(sea_f15plus)
fore_wprf15plus <- fore2020(wpr_f15plus)
bound_f15plus <- cbind(fore_afrf15plus, fore_amrf15plus$Number, fore_emrf15plus$Number, fore_eurf15plus$Number, fore_seaf15plus$Number, fore_wprf15plus$Number)
colnames(bound_f15plus) <- c("AFR", "Year", "AMR", "EMR", "EUR", "SEA", "WPR")
bound_f15plus <- bound_f15plus[, c(2, 1, 3:7)]
Attempt 2 at creating the time series analyses
#males04 plot
males_04 <- select(perc_world, c(g_whoregion, year, newrel_m04))
gg_males04 <- ggplot(males_04, aes(x = year, y = newrel_m04, col = g_whoregion)) + geom_line() + geom_point(x = 2020, y = 27043, col = "red") + geom_point(x = 2020, y = 2062, col = "brown") + geom_point(x = 2020, y = 17243.17, col = "green") + geom_point(x = 2020, y = 1465, col = "turquoise2") + geom_point(x = 2020, y = 58951.83, col = "blue") + geom_point(x = 2020, y = 12558, col = "pink") + scale_y_continuous(limits = c(0, 60000)) + labs(title = "Notifications Time Series for Males 0-4 yrs")
#females 04 plot
females_04 <- select(perc_world, c(g_whoregion, year, newrel_f04))
gg_females04 <- ggplot(females_04, aes(x = year, y = newrel_f04, col = g_whoregion)) + geom_line() + geom_point(x = 2020, y = 23790, col = "red") + geom_point(x = 2020, y = 1776, col = "brown") + geom_point(x = 2020, y = 13286.17, col = "green") + geom_point(x = 2020, y = 1269, col = "turquoise2") + geom_point(x = 2020, y = 43694, col = "blue") + geom_point(x = 2020, y = 10140, col = "pink") + scale_y_continuous(limits = c(0, 50000)) + labs(title = "Notifications Time Series for Females 0-4 yrs")
#males 514 plot
males_514 <- select(perc_world, c(g_whoregion, year, newrel_m514))
gg_males514 <- ggplot(males_514, aes(x = year, y = newrel_m514, col = g_whoregion)) + geom_line() + geom_point(x = 2020, y = 32352, col = "red") + geom_point(x = 2020, y = 2826, col = "brown") + geom_point(x = 2020, y = 18620, col = "green") + geom_point(x = 2020, y = 3057, col = "turquoise2") + geom_point(x = 2020, y = 82535, col = "blue") + geom_point(x = 2020, y = 19606, col = "pink") + scale_y_continuous(limits = c(0, 90000)) + labs(title = "Notifications Time Series for Males 5-14 yrs")
#females514
females_514 <- select(perc_world, c(g_whoregion, year, newrel_f514))
gg_females514 <- ggplot(females_514, aes(x = year, y = newrel_f514, col = g_whoregion)) + geom_line() + geom_point(x = 2020, y = 32411, col = "red") + geom_point(x = 2020, y = 2897, col = "brown") + geom_point(x = 2020, y = 21632, col = "green") + geom_point(x = 2020, y = 2953, col = "turquoise2") + geom_point(x = 2020, y = 87086, col = "blue") + geom_point(x = 2020, y = 17500, col = "pink") + scale_y_continuous(limits = c(0, 100000)) + labs(title = "Notifications Time Series for Females 5-14 yrs")
#males 15 plus plot
males_15plus <- select(perc_world, c(g_whoregion, year, newrel_m15plus))
gg_males15plus <- ggplot(males_15plus, aes(x = year, y = newrel_m15plus, col = g_whoregion)) + geom_line() + geom_point(x = 2020, y = 661953, col = "red") + geom_point(x = 2020, y = 154305.3, col = "brown") + geom_point(x = 2020, y = 226294, col = "green") + geom_point(x = 2020, y = 129819.7, col = "turquoise2") + geom_point(x = 2020, y = 1931341, col = "blue") + geom_point(x = 2020, y = 891849, col = "pink") + scale_y_continuous(limits = c(0, 2000000)) + labs(title = "Notifications Time Series for Males 15plus yrs")
#females 15 plus plot
females_15plus <- select(perc_world, c(g_whoregion, year, newrel_f15plus))
gg_females15plus <- ggplot(females_15plus, aes(x = year, y = newrel_f15plus, col = g_whoregion)) + geom_line() + geom_point(x = 2020, y = 426790, col = "red") + geom_point(x = 2020, y = 77417, col = "brown") + geom_point(x = 2020, y = 201386, col = "green") + geom_point(x = 2020, y = 66222.5, col = "turquoise2") + geom_point(x = 2020, y = 1182645, col = "blue") + geom_point(x = 2020, y = 435651, col = "pink") + scale_y_continuous(limits = c(0, 1200000)) + labs(title = "Notifications Time Series for Females 15plus yrs")
Creating notification graphs for entire world
#Putting together notification data for years 2013 to 2020
world_data <- world_2013[,-c(1:3)] %>% group_by(year) %>% na.omit()
tot_m04 <- summarise(world_data, total = sum(newrel_m04))
tot_m04 %<>% as.data.frame()
colnames(tot_m04) <- c("year", "m04")
tot_f04 <- summarise(world_data, total = sum(newrel_f04))
tot_f04 %<>% as.data.frame()
colnames(tot_f04) <- c("year", "f04")
tot_m514 <- summarise(world_data, total = sum(newrel_m514))
tot_m514 %<>% as.data.frame()
colnames(tot_m514) <- c("year", "m514")
tot_f514 <- summarise(world_data, total = sum(newrel_f514))
tot_f514 %<>% as.data.frame()
colnames(tot_f514) <- c("year", "f514")
tot_m15plus <- summarise(world_data, total = sum(newrel_m15plus))
tot_m15plus %<>% as.data.frame()
colnames(tot_m15plus) <- c("year", "m15plus")
tot_f15plus <- summarise(world_data, total = sum(newrel_f15plus))
tot_f15plus %<>% as.data.frame()
colnames(tot_f15plus) <- c("year", "f15plus")
all_tots <- cbind(tot_m04, tot_f04$f04, tot_m514$m514, tot_f514$f514, tot_m15plus$m15plus, tot_f15plus$f15plus)
colnames(all_tots) <- c("year", "case_m04", "case_f04", "case_m514", "case_f514", "case_m15plus", "case_f15plus")
all_tots <- mutate(all_tots, "all04" = case_m04 + case_f04, "all514" = case_m514 + case_f514, "all15plus" = case_m15plus + case_f15plus, "all_male" = case_m04 + case_m514 + case_m15plus, "all_female" = case_f04 + case_f514 + case_f15plus, "all_cases" = all_male + all_female)
all_copy <- all_tots
all_copy04 <- all_copy[, c(1:3)]
colnames(all_copy04) <- c("year", "male", "female")
all_copy04 <- pivot_longer(all_copy04, cols = c(male, female), names_to = "sex", values_to = "cases")
all_copy514 <- all_copy[, c(1,4,5)]
colnames(all_copy514) <- c("year", "male", "female")
all_copy514 <- pivot_longer(all_copy514, cols = c(male, female), names_to = "sex", values_to = "cases")
all_copy15plus <- all_copy[, c(1,6,7)]
colnames(all_copy15plus) <- c("year", "male", "female")
all_copy15plus <- pivot_longer(all_copy15plus, cols = c(male, female), names_to = "sex", values_to = "cases")
all_copy15plus <- all_copy[, c(1,6,7)]
colnames(all_copy15plus) <- c("year", "male", "female")
all_copy15plus <- pivot_longer(all_copy15plus, cols = c(male, female), names_to = "sex", values_to = "cases")
###Drawing the plots
world_04 <- ggplot(all_copy04, aes(x = year, y = cases, fill = sex)) + geom_bar(stat = "identity", position = "dodge") + labs(title = "Number of Reported Cases Amongst 0-4 yrs from 2013 to 2020")
world_514 <- ggplot(all_copy514, aes(x = year, y = cases, fill = sex)) + geom_bar(stat = "identity", position = "dodge") + labs(title = "Number of Reported Cases Amongst 5-14 yrs from 2013 to 2020")
world_15plus <- ggplot(all_copy15plus, aes(x = year, y = cases, fill = sex)) + geom_bar(stat = "identity", position = "dodge") + labs(title = "Number of Reported Cases Amongst 15plus yrs from 2013 to 2020")
Repeating the continental difference between actual and predicted and including column for overall
---
title: "All Countries Regional Data"
output: html_notebook
---

AIM: Create region by region analysis of male vs female for each age groups (0-4, 5-14, 0-14 and 15 plus) for all countries separated by region

```{r}
setwd("~/Desktop/AFP/modV3/data")
library(dplyr)
library(tidyverse)
library(tidyr)
library(forecast)
library(fpp2)
library(stringr)
library(magrittr)
library(ggplot2)

world <- read_csv("TB_notifications.csv")
tidyworld <- world[, c(1,3,5,6,100,103,104,113,115,118,119,128)]
world_2013 <- tidyworld %>% filter(year > 2012)
```

Organising the data by g_whoregion

```{r}
grouped_2013 <- group_by(world_2013, g_whoregion) %>% filter(year == 2013) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)), 
                                                                                         newrel_f04 = sum(na.omit(newrel_f04)),
                 newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)), 
                 newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)), 
                 newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2013)

grouped_2014 <- group_by(world_2013, g_whoregion) %>% filter(year == 2014) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)), 
                                                                                         newrel_f04 = sum(na.omit(newrel_f04)),
                 newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)), 
                 newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)), 
                 newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2014)

grouped_2015 <- group_by(world_2013, g_whoregion) %>% filter(year == 2015) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)), 
                                                                                         newrel_f04 = sum(na.omit(newrel_f04)),
                 newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)), 
                 newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)), 
                 newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2015)

grouped_2016 <- group_by(world_2013, g_whoregion) %>% filter(year == 2016) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)), 
                                                                                         newrel_f04 = sum(na.omit(newrel_f04)),
                 newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)), 
                 newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)), 
                 newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2016)

grouped_2017 <- group_by(world_2013, g_whoregion) %>% filter(year == 2017) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)), 
                                                                                         newrel_f04 = sum(na.omit(newrel_f04)),
                 newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)), 
                 newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)), 
                 newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2017)

grouped_2018 <- group_by(world_2013, g_whoregion) %>% filter(year == 2018) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)), 
                                                                                         newrel_f04 = sum(na.omit(newrel_f04)),
                 newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)), 
                 newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)), 
                 newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2018)

grouped_2019 <- group_by(world_2013, g_whoregion) %>% filter(year == 2019) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)), 
                                                                                         newrel_f04 = sum(na.omit(newrel_f04)),
                 newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)), 
                 newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)), 
                 newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2019)

grouped_2020 <- group_by(world_2013, g_whoregion) %>% filter(year == 2020) %>% summarise(newrel_m04 = sum(na.omit(newrel_m04)), 
                                                                                         newrel_f04 = sum(na.omit(newrel_f04)),
                 newrel_m514 = sum(na.omit(newrel_m514)), newrel_f514 = sum(na.omit(newrel_f514)), 
                 newrel_m014 = sum(na.omit(newrel_m014)), newrel_f014 = sum(na.omit(newrel_f014)), 
                 newrel_m15plus = sum(na.omit(newrel_m15plus)), newrel_f15plus = sum(na.omit(newrel_f15plus))) %>% as.data.frame() %>% cbind(year = 2020)

bind_world <- rbind(grouped_2013, grouped_2014, grouped_2015, grouped_2016, grouped_2017, grouped_2018, grouped_2019, grouped_2020)

bind_world <- bind_world[, c(1, 10, 2:9)]

arrange_world <- arrange(bind_world, g_whoregion)

##Including columns for total values 

mutate_world <- mutate(arrange_world, "newrel_tot04" = newrel_m04 + newrel_f04, "newrel_tot514" = newrel_m514 + newrel_f514, "newrel_tot014" = newrel_m014 + newrel_f014, "newrel_tot15plus" = newrel_m15plus + newrel_f15plus)

perc_world <- mutate(mutate_world, "perc_014" = 100 * (newrel_tot014/(newrel_tot014 + newrel_tot15plus)), "perc_youngkids" = 100 * (newrel_tot04/newrel_tot014))



```

Creating graphs for male vs female for 0-4 by g_whoregion

```{r}
afr_04 <- perc_world %>% filter(g_whoregion == "AFR") %>% select(newrel_m04, newrel_f04) %>% 
  t()
colnames(afr_04) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
afr_04 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,30000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-4 years in AFR")

amr_04 <- perc_world %>% filter(g_whoregion == "AMR") %>% select(newrel_m04, newrel_f04) %>% 
  t()
colnames(amr_04) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
amr_04 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,3000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-4 years in AMR")

emr_04 <- perc_world %>% filter(g_whoregion == "EMR") %>% select(newrel_m04, newrel_f04) %>% 
  t()
colnames(emr_04) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
emr_04 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,20000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-4 years in EMR")

eur_04 <- perc_world %>% filter(g_whoregion == "EUR") %>% select(newrel_m04, newrel_f04) %>% 
  t()
colnames(eur_04) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
eur_04 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,3000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-4 years in EUR")

sea_04 <- perc_world %>% filter(g_whoregion == "SEA") %>% select(newrel_m04, newrel_f04) %>% 
  t() 
colnames(sea_04) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
sea_04 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,60000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-4 years in SEA")

wpr_04 <- perc_world %>% filter(g_whoregion == "WPR") %>% select(newrel_m04, newrel_f04) %>% 
  t()
colnames(wpr_04) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
wpr_04 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,20000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-4 years in WPR")


```
Creating graphs for notifications in 5-14 years by g_whoregion

```{r}
afr_514 <- perc_world %>% filter(g_whoregion == "AFR") %>% select(newrel_m514, newrel_f514) %>% 
  t() 
colnames(afr_514) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
afr_514 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,40000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-514 years in AFR")

amr_514 <- perc_world %>% filter(g_whoregion == "AMR") %>% select(newrel_m514, newrel_f514) %>% 
  t() 
colnames(afr_514) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
amr_514 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,4000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 5-14 years in AMR")

emr_514 <- perc_world %>% filter(g_whoregion == "EMR") %>% select(newrel_m514, newrel_f514) %>% 
  t() 
colnames(emr_514) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
emr_514 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,30000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 5-14 years in EMR")

eur_514 <- perc_world %>% filter(g_whoregion == "EUR") %>% select(newrel_m514, newrel_f514) %>% 
  t()
colnames(eur_514) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
eur_514 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,5000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 5-14 years in EUR")

sea_514 <- perc_world %>% filter(g_whoregion == "SEA") %>% select(newrel_m514, newrel_f514) %>% 
  t() 
colnames(sea_514) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
sea_514 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,100000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 5-14 years in SEA")

wpr_514 <- perc_world %>% filter(g_whoregion == "WPR") %>% select(newrel_m514, newrel_f514) %>% 
  t()
colnames(wpr_514) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
wpr_514 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,30000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 5-14 years in WPR")
```
Creating plot for 0-14 

```{r}
afr_014 <- perc_world %>% filter(g_whoregion == "AFR") %>% select(newrel_m014, newrel_f014) %>% 
  t()
colnames(afr_014) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
afr_014 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,70000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-14 years in AFR")

amr_014 <- perc_world %>% filter(g_whoregion == "AMR") %>% select(newrel_m014, newrel_f014) %>% 
  t()
colnames(amr_014) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
amr_014 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,6000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-14 years in AMR")

emr_014 <- perc_world %>% filter(g_whoregion == "EMR") %>% select(newrel_m014, newrel_f014) %>% 
  t() 
colnames(emr_014) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
emr_014 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,50000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-14 years in EMR")

eur_014 <- perc_world %>% filter(g_whoregion == "EUR") %>% select(newrel_m014, newrel_f014) %>% 
  t() 
colnames(eur_014) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
eur_014 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,8000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-14 years in EUR")

sea_014 <- perc_world %>% filter(g_whoregion == "SEA") %>% select(newrel_m014, newrel_f014) %>% 
  t()
colnames(sea_014) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
sea_014 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,150000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-14 years in SEA")

wpr_014 <- perc_world %>% filter(g_whoregion == "WPR") %>% select(newrel_m014, newrel_f014) %>% 
  t() 
colnames(wpr_014) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
wpr_014 %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,50000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 0-14 years in WPR")
```
Creating plot for 15 plus 

```{r}
afr_15plus <- perc_world %>% filter(g_whoregion == "AFR") %>% select(newrel_m15plus, newrel_f15plus) %>% 
  t() 
colnames(afr_15plus) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
afr_15plus %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,1000000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 15plus years in AFR")

amr_15plus <- perc_world %>% filter(g_whoregion == "AMR") %>% select(newrel_m15plus, newrel_f15plus) %>% 
  t()
colnames(amr_15plus) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
amr_15plus %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,300000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 15 plus years in AMR")

emr_15plus <- perc_world %>% filter(g_whoregion == "EMR") %>% select(newrel_m15plus, newrel_f15plus) %>% 
  t() 
colnames(emr_15plus) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
emr_15plus %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,500000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 15plus years in EMR")

eur_15plus <- perc_world %>% filter(g_whoregion == "EUR") %>% select(newrel_m15plus, newrel_f15plus) %>% 
  t() 
colnames(eur_15plus) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
eur_15plus %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,200000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 15plus years in EUR")

sea_15plus <- perc_world %>% filter(g_whoregion == "SEA") %>% select(newrel_m15plus, newrel_f15plus) %>% 
  t() 
colnames(sea_15plus) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
sea_15plus %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,2000000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 15plus years in SEA")

wpr_15plus <- perc_world %>% filter(g_whoregion == "WPR") %>% select(newrel_m15plus, newrel_f15plus) %>% 
  t() 
colnames(wpr_15plus) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
wpr_15plus %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,1000000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for ages 15plus years in WPR")
```
Plotting total notifications over time 

```{r}
tot_world <- mutate(perc_world, "newrel_mtot" = newrel_m014 + newrel_m15plus, "newrel_ftot" = newrel_f014 + newrel_f15plus, "TOT" = newrel_mtot + newrel_ftot)

afr_tot <- tot_world %>% filter(g_whoregion == "AFR") %>% select(newrel_mtot, newrel_ftot) %>% 
  t() 
colnames(afr_tot) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
afr_tot %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,1000000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for all ages years in AFR")

amr_tot <- tot_world %>% filter(g_whoregion == "AMR") %>% select(newrel_mtot, newrel_ftot) %>% 
  t()
colnames(amr_tot) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
amr_tot %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,200000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for all ages years in AMR")

emr_tot <- tot_world %>% filter(g_whoregion == "EMR") %>% select(newrel_mtot, newrel_ftot) %>% 
  t() 
colnames(emr_tot) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
emr_tot %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,500000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for all ages years in EMR")

eur_tot <- tot_world %>% filter(g_whoregion == "EUR") %>% select(newrel_mtot, newrel_ftot) %>% 
  t()
colnames(eur_tot) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
eur_tot %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,200000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for all ages years in EUR")

sea_tot <- tot_world %>% filter(g_whoregion == "SEA") %>% select(newrel_mtot, newrel_ftot) %>% 
  t() 
colnames(sea_tot) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
sea_tot %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,3000000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for all ages years in SEA")

wpr_tot <- tot_world %>% filter(g_whoregion == "WPR") %>% select(newrel_mtot, newrel_ftot) %>% 
  t() 
colnames(wpr_tot) <- c("2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020")
wpr_tot %<>% barplot(col = c("skyblue", "seagreen1"), beside = TRUE, ylim = c(0,1500000), xlab = "year", ylab = "notifications") + title("Sex disaggregated notifications for all ages years in WPR")

```
```{r}
##Attempt at creating a forecast 

filt_afr04 <- perc_world %>% filter(g_whoregion == "AFR") %>% filter(year != 2020)

ts_afr04 <- ts(filt_afr04[, c(3)], start = 2013, frequency = 1)

arima_afr04 <- auto.arima(ts_afr04, d=1, D=1, stepwise = FALSE, approximation = FALSE, trace = TRUE)
print(summary(arima_afr04))
checkresiduals(arima_afr04)

fcst_afrm04 <- forecast(arima_afr04, h = 1)
autoplot(fcst)
sum_afrm04 <- print(summary(fcst))

####Attempt at creating function to produce predictions 

out_2020 <- function(df = tot_world, region){
        df %>% filter(year != 2020) %>% filter(g_whoregion == region) %>% return() }

arima_TB <- function(df = tot_world, group) {
        data1 <- df %>% select(group) %>% ts(start = 2013, frequency = 1) %>% auto.arima(d=1, D=1, stepwise = FALSE, approximation = FALSE, trace = TRUE) 
        print(summary(data1))
        FCST <- forecast(data1, h = 1)
        autoplot(FCST)
        print(summary(FCST))}

##AFRICA ESTIMATES 

afr_m04 <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_m04") 
rownames(afr_m04) <- "afr_m04"

afr_f04 <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_f04")
rownames(afr_f04) <- "afr_f04"

afr_m514 <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_m514")
rownames(afr_m514) <- "afr_m514"

afr_f514 <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_f514")
rownames(afr_f514) <- "afr_f514"

afr_m014 <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_m014")
rownames(afr_m014) <- "afr_m014"

afr_f014 <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_f014")
rownames(afr_f014) <- "afr_f014"

afr_m15plus <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_m15plus")
rownames(afr_m15plus) <- "afr_m15plus"

afr_f15plus <- out_2020(tot_world, "AFR") %>% arima_TB("newrel_f15plus")
rownames(afr_f15plus) <- "afr_f15plus"

afr_estimates <- rbind(afr_m04, afr_f04, afr_m514, afr_f514, afr_m014, afr_f014, afr_m15plus, afr_f15plus)


#SEA predictions 
sea_m04 <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_m04") 
rownames(sea_m04) <- "sea_m04"

sea_f04 <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_f04")
rownames(sea_f04) <- "sea_f04"

sea_m514 <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_m514")
rownames(sea_m514) <- "sea_m514"

sea_f514 <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_f514")
rownames(sea_f514) <- "sea_f514"

sea_m014 <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_m014")
rownames(sea_m014) <- "sea_m014"

sea_f014 <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_f014")
rownames(sea_f014) <- "sea_f014"

sea_m15plus <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_m15plus")
rownames(sea_m15plus) <- "sea_m15plus"

sea_f15plus <- out_2020(tot_world, "SEA") %>% arima_TB("newrel_f15plus")
rownames(sea_f15plus) <- "sea_f15plus"

sea_estimates <- rbind(sea_m04, sea_f04, sea_m514, sea_f514, sea_m014, sea_f014, sea_m15plus, sea_f15plus)

#WPR estimates 

wpr_m04 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m04") 
rownames(wpr_m04) <- "wpr_m04"

wpr_f04 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f04")
rownames(wpr_f04) <- "wpr_f04"

wpr_m514 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m514")
rownames(wpr_m514) <- "wpr_m514"

wpr_f514 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f514")
rownames(wpr_f514) <- "wpr_f514"

wpr_m014 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m014")
rownames(wpr_m014) <- "wpr_m014"

wpr_f014 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f014")
rownames(wpr_f014) <- "wpr_f014"

wpr_m15plus <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m15plus")
rownames(wpr_m15plus) <- "wpr_m15plus"

wpr_f15plus <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f15plus")
rownames(wpr_f15plus) <- "wpr_f15plus"

wpr_estimates <- rbind(wpr_m04, wpr_f04, wpr_m514, wpr_f514, wpr_m014, wpr_f014, wpr_m15plus, wpr_f15plus)

#EUR Estimates

eur_m04 <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_m04") 
rownames(eur_m04) <- "eur_m04"

eur_f04 <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_f04")
rownames(eur_f04) <- "eur_f04"

eur_m514 <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_m514")
rownames(eur_m514) <- "eur_m514"

eur_f514 <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_f514")
rownames(eur_f514) <- "eur_f514"

eur_m014 <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_m014")
rownames(eur_m014) <- "eur_m014"

eur_f014 <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_f014")
rownames(eur_f014) <- "eur_f014"

eur_m15plus <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_m15plus")
rownames(eur_m15plus) <- "eur_m15plus"

eur_f15plus <- out_2020(tot_world, "EUR") %>% arima_TB("newrel_f15plus")
rownames(eur_f15plus) <- "eur_f15plus"

eur_estimates <- rbind(eur_m04, eur_f04, eur_m514, eur_f514, eur_m014, eur_f014, eur_m15plus, eur_f15plus)

#AMR Estimates

emr_m04 <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_m04") 
rownames(emr_m04) <- "emr_m04"

emr_f04 <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_f04")
rownames(emr_f04) <- "emr_f04"

emr_m514 <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_m514")
rownames(emr_m514) <- "emr_m514"

emr_f514 <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_f514")
rownames(emr_f514) <- "emr_f514"

emr_m014 <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_m014")
rownames(emr_m014) <- "emr_m014"

emr_f014 <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_f014")
rownames(emr_f014) <- "emr_f014"

emr_m15plus <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_m15plus")
rownames(emr_m15plus) <- "emr_m15plus"

emr_f15plus <- out_2020(tot_world, "EMR") %>% arima_TB("newrel_f15plus")
rownames(emr_f15plus) <- "emr_f15plus"

emr_estimates <- rbind(emr_m04, emr_f04, emr_m514, emr_f514, emr_m014, emr_f014, emr_m15plus, emr_f15plus)

#WPR Estimates

wpr_m04 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m04") 
rownames(wpr_m04) <- "wpr_m04"

wpr_f04 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f04")
rownames(wpr_f04) <- "wpr_f04"

wpr_m514 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m514")
rownames(wpr_m514) <- "wpr_m514"

wpr_f514 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f514")
rownames(wpr_f514) <- "wpr_f514"

wpr_m014 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m014")
rownames(wpr_m014) <- "wpr_m014"

wpr_f014 <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f014")
rownames(wpr_f014) <- "wpr_f014"

wpr_m15plus <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_m15plus")
rownames(wpr_m15plus) <- "wpr_m15plus"

wpr_f15plus <- out_2020(tot_world, "WPR") %>% arima_TB("newrel_f15plus")
rownames(wpr_f15plus) <- "wpr_f15plus"

wpr_estimates <- rbind(wpr_m04, wpr_f04, wpr_m514, wpr_f514, wpr_m014, wpr_f014, wpr_m15plus, wpr_f15plus)

#AMR Estimates

amr_m04 <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_m04") 
rownames(amr_m04) <- "amr_m04"

amr_f04 <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_f04")
rownames(amr_f04) <- "amr_f04"

amr_m514 <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_m514")
rownames(amr_m514) <- "amr_m514"

amr_f514 <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_f514")
rownames(amr_f514) <- "amr_f514"

amr_m014 <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_m014")
rownames(amr_m014) <- "amr_m014"

amr_f014 <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_f014")
rownames(amr_f014) <- "amr_f014"

amr_m15plus <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_m15plus")
rownames(amr_m15plus) <- "amr_m15plus"

amr_f15plus <- out_2020(tot_world, "AMR") %>% arima_TB("newrel_f15plus")
rownames(amr_f15plus) <- "amr_f15plus"

amr_estimates <- rbind(amr_m04, amr_f04, amr_m514, amr_f514, amr_m014, amr_f014, amr_m15plus, amr_f15plus)

```
Combining the data for 2020 with the model estimates 

```{r}
##AFR difference calculations 
afr_2020 <- tot_world %>% filter(year == 2020) %>% filter(g_whoregion == "AFR") %>% t() 
afr_2020 <- as.data.frame(afr_2020[c(3:10), ])

rownames(afr_2020) <- c("afr_m04", "afr_f04", "afr_m514", "afr_f514", "afr_m014", "afr_f014", "afr_m15plus", "afr_f15plus")
colnames(afr_2020) <- "notif_2020"

est_afr_2020 <- cbind(afr_2020, afr_estimates)
colnames(est_afr_2020) <- c("notif_2020", "est_2020", "Lo_80", "Hi_80", "Lo_95", "Hi_95")
est_afr_2020$notif_2020 <- as.numeric(est_afr_2020$notif_2020)

dif_afr <- mutate(est_afr_2020, "Difference" = notif_2020 - est_2020, "afr_perc" = 100*(Difference/est_2020))

##SEA difference calculations 
sea_2020 <- tot_world %>% filter(year == 2020) %>% filter(g_whoregion == "SEA") %>% t() 
sea_2020 <- as.data.frame(sea_2020[c(3:10), ])

rownames(sea_2020) <- c("sea_m04", "sea_f04", "sea_m514", "sea_f514", "sea_m014", "sea_f014", "sea_m15plus", "sea_f15plus")
colnames(sea_2020) <- "notif_2020"
est_sea_2020 <- cbind(sea_2020, sea_estimates)
colnames(est_sea_2020) <- c("notif_2020", "est_2020", "Lo_80", "Hi_80", "Lo_95", "Hi_95")
est_sea_2020$notif_2020 <- as.numeric(est_sea_2020$notif_2020)

dif_sea <- mutate(est_sea_2020, "Difference" = notif_2020 - est_2020, "sea_perc" = 100*(Difference/est_2020))

#WPR difference calculations 
wpr_2020 <- tot_world %>% filter(year == 2020) %>% filter(g_whoregion == "WPR") %>% t() 
wpr_2020 <- as.data.frame(wpr_2020[c(3:10), ])

rownames(wpr_2020) <- c("wpr_m04", "wpr_f04", "wpr_m514", "wpr_f514", "wpr_m014", "wpr_f014", "wpr_m15plus", "wpr_f15plus")
colnames(wpr_2020) <- "wpr_2020"
est_wpr_2020 <- cbind(wpr_2020, wpr_estimates)
colnames(est_wpr_2020) <- c("notif_2020", "est_2020", "Lo_80", "Hi_80", "Lo_95", "Hi_95")
est_wpr_2020$notif_2020 <- as.numeric(est_wpr_2020$notif_2020)

dif_wpr <- mutate(est_wpr_2020, "Difference" = notif_2020 - est_2020, "wpr_perc" = 100*(Difference/est_2020))

#EUR difference calculations 
eur_2020 <- tot_world %>% filter(year == 2020) %>% filter(g_whoregion == "EUR") %>% t() 
eur_2020 <- as.data.frame(eur_2020[c(3:10), ])

rownames(eur_2020) <- c("eur_m04", "eur_f04", "eur_m514", "eur_f514", "eur_m014", "eur_f014", "eur_m15plus", "eur_f15plus")
colnames(eur_2020) <- "eur_2020"
est_eur_2020 <- cbind(eur_2020, eur_estimates)
colnames(est_eur_2020) <- c("notif_2020", "est_2020", "Lo_80", "Hi_80", "Lo_95", "Hi_95")
est_eur_2020$notif_2020 <- as.numeric(est_eur_2020$notif_2020)

dif_eur <- mutate(est_eur_2020, "Difference" = notif_2020 - est_2020, "eur_perc" = 100*(Difference/est_2020)) 

#AMR difference calculations
amr_2020 <- tot_world %>% filter(year == 2020) %>% filter(g_whoregion == "AMR") %>% t() 
amr_2020 <- as.data.frame(amr_2020[c(3:10), ])

rownames(amr_2020) <- c("amr_m04", "amr_f04", "amr_m514", "amr_f514", "amr_m014", "amr_f014", "amr_m15plus", "amr_f15plus")
colnames(eur_2020) <- "amr_2020"
est_amr_2020 <- cbind(amr_2020, amr_estimates)
colnames(est_amr_2020) <- c("notif_2020", "est_2020", "Lo_80", "Hi_80", "Lo_95", "Hi_95")
est_amr_2020$notif_2020 <- as.numeric(est_amr_2020$notif_2020)

dif_amr <- mutate(est_amr_2020, "Difference" = notif_2020 - est_2020, "amr_perc" = 100*(Difference/est_2020)) 

#EMR difference calculations
emr_2020 <- tot_world %>% filter(year == 2020) %>% filter(g_whoregion == "EMR") %>% t() 
emr_2020 <- as.data.frame(emr_2020[c(3:10), ])

rownames(emr_2020) <- c("emr_m04", "emr_f04", "emr_m514", "emr_f514", "emr_m014", "emr_f014", "emr_m15plus", "emr_f15plus")
colnames(emr_2020) <- "emr_2020"
est_emr_2020 <- cbind(emr_2020, emr_estimates)
colnames(est_emr_2020) <- c("notif_2020", "est_2020", "Lo_80", "Hi_80", "Lo_95", "Hi_95")
est_emr_2020$notif_2020 <- as.numeric(est_emr_2020$notif_2020)

dif_emr <- mutate(est_emr_2020, "Difference" = notif_2020 - est_2020, "emr_perc" = 100*(Difference/est_2020)) 

#Combined data frame with difference data 
combined_dif <- data.frame(dif_afr[, c(7,8)], dif_amr[,c(7,8)], dif_emr[, c(7,8)], dif_eur[,c(7:8)], dif_sea[,c(7:8)], dif_wpr[,c(7:8)])
combined_dif$group <- as.vector(rownames(combined_dif))
combined_dif$group <- c("m04", "f04", "m514", "f514", "m014", "f014", "m15plus", "f15plus")
rownames(combined_dif) <- c(1:8)
combined_dif <- combined_dif[, c(13,1:12)]

colnames(combined_dif) <- c("group", "AFR_dif", "AFR_perc", "AMR_dif", "AMR_perc", "EMR_dif", "EMR_perc", "EUR_dif", "EUR_perc", "SEA_dif", "SEA_perc", "WPR_dif", "WPR_perc")

#isolating only the percentage data to be able to plot a bar plot 
bar_data <- combined_dif[, c(1,3,5,7,9,11,13)]
bar_newgroup <- str_split_fixed(bar_data$group, "", 2)
bar_data2 <- cbind(bar_data, bar_newgroup)
colnames(bar_data2) <- c("group", "AFR", "AMR", "EMR", "EUR", "SEA", "WPR", "sex", "age_group")
bar_data2 <- bar_data2[, c(9,8,2:7)]


piv_dif <- pivot_longer(bar_data2, cols = c("AFR", "AMR", "EMR", "EUR", "SEA", "WPR"), names_to = "g_whoregion", values_to = "value")

library(dplyr)
library(ggplot2)
piv_three <- filter(piv_dif, age_group != "014")

##Attempt at drawing plots  including 0-14 
arima_dif <- ggplot(piv_dif, aes(x = g_whoregion, y = value, fill = sex)) + facet_wrap(~piv_dif$age_group) + geom_bar(stat = "identity", position = "dodge")

##Attempt excluding 0-14 
piv_copy <- piv_three
piv_copy$age_group <- factor(piv_copy$age_group, levels = c("04", "514", "15plus"))
arima_copy <- ggplot(piv_copy, aes(x = g_whoregion, y = value, fill = sex)) + facet_wrap(~piv_copy$age_group) + geom_bar(stat = "identity", position = "dodge")
arima_copy

arima_three <- ggplot(piv_three, aes(x = g_whoregion, y = value, fill = sex)) + facet_wrap(~piv_three$age_group) + geom_bar(stat = "identity", position = "dodge")
arima_three

``` 



```{r}
#Attempt at the forecasting models 
#Rearranging data for 0-4 males 

males_04 <- select(perc_world, c(g_whoregion, year, newrel_m04)) 
piv_na <- pivot_wider(males_04, names_from = "year", values_from = "newrel_m04")
piv_na$`2020` <- NA
males_back <- pivot_longer(piv_na, cols = c(as.vector(colnames(piv_na[,2:9]))), names_to = "year")
males_back$year <- as.numeric(males_back$year)
males_no_pivot <- males_04
males_pivot <- pivot_wider(males_04, names_from = "g_whoregion", values_from = "newrel_m04")

select(afr_m04, "Point Forecast")

fore2020 <- function(df){
  a <- select(df, "Point Forecast")
  b <- "2020"
  colnames(a) <- "Number"
  rownames(a) <- NULL
  a$Year <- b
  return(a)
}

#males 04 
fore_afrm04 <- fore2020(afr_m04)
fore_amrm04 <- fore2020(amr_m04)
fore_emrm04 <- fore2020(emr_m04)
fore_eurm04 <- fore2020(eur_m04)
fore_seam04 <- fore2020(sea_m04)
fore_wprm04 <- fore2020(wpr_m04)

bound_fore <- cbind(fore_afrm04, fore_amrm04$Number, fore_emrm04$Number, fore_eurm04$Number, fore_seam04$Number, fore_wprm04$Number)

colnames(bound_fore) <- c("AFR", "Year", "AMR", "EMR", "EUR", "SEA", "WPR")
bound_fore <- bound_fore[, c(2, 1, 3:7)]

#females04
fore_afrf04 <- fore2020(afr_f04)
fore_amrf04 <- fore2020(amr_f04)
fore_emrf04 <- fore2020(emr_f04)
fore_eurf04 <- fore2020(eur_f04)
fore_seaf04 <- fore2020(sea_f04)
fore_wprf04 <- fore2020(wpr_f04)

bound_f04 <- cbind(fore_afrf04, fore_amrf04$Number, fore_emrf04$Number, fore_eurf04$Number, fore_seaf04$Number, fore_wprf04$Number)

colnames(bound_f04) <- c("AFR", "Year", "AMR", "EMR", "EUR", "SEA", "WPR")
bound_f04 <- bound_f04[, c(2, 1, 3:7)]

#males 514 
fore_afrm514 <- fore2020(afr_m514)
fore_amrm514 <- fore2020(amr_m514)
fore_emrm514 <- fore2020(emr_m514)
fore_eurm514 <- fore2020(eur_m514)
fore_seam514 <- fore2020(sea_m514)
fore_wprm514 <- fore2020(wpr_m514)

bound_m514 <- cbind(fore_afrm514, fore_amrm514$Number, fore_emrm514$Number, fore_eurm514$Number, fore_seam514$Number, fore_wprm514$Number)

colnames(bound_m514) <- c("AFR", "Year", "AMR", "EMR", "EUR", "SEA", "WPR")
bound_m514 <- bound_m514[, c(2, 1, 3:7)]

#females 514 
fore_afrf514 <- fore2020(afr_f514)
fore_amrf514 <- fore2020(amr_f514)
fore_emrf514 <- fore2020(emr_f514)
fore_eurf514 <- fore2020(eur_f514)
fore_seaf514 <- fore2020(sea_f514)
fore_wprf514 <- fore2020(wpr_f514)

bound_f514 <- cbind(fore_afrf514, fore_amrf514$Number, fore_emrf514$Number, fore_eurf514$Number, fore_seaf514$Number, fore_wprf514$Number)

colnames(bound_f514) <- c("AFR", "Year", "AMR", "EMR", "EUR", "SEA", "WPR")
bound_f514 <- bound_f514[, c(2, 1, 3:7)]

#males 15plus 
fore_afrm15plus <- fore2020(afr_m15plus)
fore_amrm15plus <- fore2020(amr_m15plus)
fore_emrm15plus <- fore2020(emr_m15plus)
fore_eurm15plus <- fore2020(eur_m15plus)
fore_seam15plus <- fore2020(sea_m15plus)
fore_wprm15plus <- fore2020(wpr_m15plus)

bound_m15plus <- cbind(fore_afrm15plus, fore_amrm15plus$Number, fore_emrm15plus$Number, fore_eurm15plus$Number, fore_seam15plus$Number, fore_wprm15plus$Number)

colnames(bound_m15plus) <- c("AFR", "Year", "AMR", "EMR", "EUR", "SEA", "WPR")
bound_m15plus <- bound_m15plus[, c(2, 1, 3:7)]

#females 15plus 
fore_afrf15plus <- fore2020(afr_f15plus)
fore_amrf15plus <- fore2020(amr_f15plus)
fore_emrf15plus <- fore2020(emr_f15plus)
fore_eurf15plus <- fore2020(eur_f15plus)
fore_seaf15plus <- fore2020(sea_f15plus)
fore_wprf15plus <- fore2020(wpr_f15plus)

bound_f15plus <- cbind(fore_afrf15plus, fore_amrf15plus$Number, fore_emrf15plus$Number, fore_eurf15plus$Number, fore_seaf15plus$Number, fore_wprf15plus$Number)

colnames(bound_f15plus) <- c("AFR", "Year", "AMR", "EMR", "EUR", "SEA", "WPR")
bound_f15plus <- bound_f15plus[, c(2, 1, 3:7)]


```

Attempt 2 at creating the time series analyses 

```{r}
#males04 plot
males_04 <- select(perc_world, c(g_whoregion, year, newrel_m04)) 
gg_males04 <- ggplot(males_04, aes(x = year, y = newrel_m04, col = g_whoregion)) + geom_line() + geom_point(x = 2020, y = 27043, col = "red") + geom_point(x = 2020, y = 2062, col = "brown") + geom_point(x = 2020, y = 17243.17, col = "green") + geom_point(x = 2020, y = 1465, col = "turquoise2") + geom_point(x = 2020, y = 58951.83, col = "blue") + geom_point(x = 2020, y = 12558, col = "pink") + scale_y_continuous(limits = c(0, 60000)) + labs(title = "Notifications Time Series for Males 0-4 yrs")

#females 04 plot
females_04 <- select(perc_world, c(g_whoregion, year, newrel_f04)) 
gg_females04 <- ggplot(females_04, aes(x = year, y = newrel_f04, col = g_whoregion)) + geom_line() + geom_point(x = 2020, y = 23790, col = "red") + geom_point(x = 2020, y = 1776, col = "brown") + geom_point(x = 2020, y = 13286.17, col = "green") + geom_point(x = 2020, y = 1269, col = "turquoise2") + geom_point(x = 2020, y = 43694, col = "blue") + geom_point(x = 2020, y = 10140, col = "pink") + scale_y_continuous(limits = c(0, 50000)) + labs(title = "Notifications Time Series for Females 0-4 yrs")

#males 514 plot 
males_514 <- select(perc_world, c(g_whoregion, year, newrel_m514)) 
gg_males514 <- ggplot(males_514, aes(x = year, y = newrel_m514, col = g_whoregion)) + geom_line() + geom_point(x = 2020, y = 32352, col = "red") + geom_point(x = 2020, y = 2826, col = "brown") + geom_point(x = 2020, y = 18620, col = "green") + geom_point(x = 2020, y = 3057, col = "turquoise2") + geom_point(x = 2020, y = 82535, col = "blue") + geom_point(x = 2020, y = 19606, col = "pink") + scale_y_continuous(limits = c(0, 90000)) + labs(title = "Notifications Time Series for Males 5-14 yrs")

#females514
females_514 <- select(perc_world, c(g_whoregion, year, newrel_f514)) 
gg_females514 <- ggplot(females_514, aes(x = year, y = newrel_f514, col = g_whoregion)) + geom_line() + geom_point(x = 2020, y = 32411, col = "red") + geom_point(x = 2020, y = 2897, col = "brown") + geom_point(x = 2020, y = 21632, col = "green") + geom_point(x = 2020, y = 2953, col = "turquoise2") + geom_point(x = 2020, y = 87086, col = "blue") + geom_point(x = 2020, y = 17500, col = "pink") + scale_y_continuous(limits = c(0, 100000)) + labs(title = "Notifications Time Series for Females 5-14 yrs")

#males 15 plus plot 
males_15plus <- select(perc_world, c(g_whoregion, year, newrel_m15plus)) 
gg_males15plus <- ggplot(males_15plus, aes(x = year, y = newrel_m15plus, col = g_whoregion)) + geom_line() + geom_point(x = 2020, y = 661953, col = "red") + geom_point(x = 2020, y = 154305.3, col = "brown") + geom_point(x = 2020, y = 226294, col = "green") + geom_point(x = 2020, y = 129819.7, col = "turquoise2") + geom_point(x = 2020, y = 1931341, col = "blue") + geom_point(x = 2020, y = 891849, col = "pink") + scale_y_continuous(limits = c(0, 2000000)) + labs(title = "Notifications Time Series for Males 15plus yrs")

#females 15 plus plot 
females_15plus <- select(perc_world, c(g_whoregion, year, newrel_f15plus)) 
gg_females15plus <- ggplot(females_15plus, aes(x = year, y = newrel_f15plus, col = g_whoregion)) + geom_line() + geom_point(x = 2020, y = 426790, col = "red") + geom_point(x = 2020, y = 77417, col = "brown") + geom_point(x = 2020, y = 201386, col = "green") + geom_point(x = 2020, y = 66222.5, col = "turquoise2") + geom_point(x = 2020, y = 1182645, col = "blue") + geom_point(x = 2020, y = 435651, col = "pink") + scale_y_continuous(limits = c(0, 1200000)) + labs(title = "Notifications Time Series for Females 15plus yrs")

```
Creating notification graphs for entire world 

```{r}
#Putting together notification data for years 2013 to 2020
world_data <- world_2013[,-c(1:3)] %>% group_by(year) %>% na.omit()
tot_m04 <- summarise(world_data, total = sum(newrel_m04))
tot_m04 %<>% as.data.frame()
colnames(tot_m04) <- c("year", "m04")

tot_f04 <- summarise(world_data, total = sum(newrel_f04))
tot_f04 %<>% as.data.frame()
colnames(tot_f04) <- c("year", "f04")

tot_m514 <- summarise(world_data, total = sum(newrel_m514))
tot_m514 %<>% as.data.frame()
colnames(tot_m514) <- c("year", "m514")

tot_f514 <- summarise(world_data, total = sum(newrel_f514))
tot_f514 %<>% as.data.frame()
colnames(tot_f514) <- c("year", "f514")

tot_m15plus <- summarise(world_data, total = sum(newrel_m15plus))
tot_m15plus %<>% as.data.frame()
colnames(tot_m15plus) <- c("year", "m15plus")

tot_f15plus <- summarise(world_data, total = sum(newrel_f15plus))
tot_f15plus %<>% as.data.frame()
colnames(tot_f15plus) <- c("year", "f15plus")

all_tots <- cbind(tot_m04, tot_f04$f04, tot_m514$m514, tot_f514$f514, tot_m15plus$m15plus, tot_f15plus$f15plus)
colnames(all_tots) <- c("year", "case_m04", "case_f04", "case_m514", "case_f514", "case_m15plus", "case_f15plus")

all_tots <- mutate(all_tots, "all04" = case_m04 + case_f04, "all514" = case_m514 + case_f514, "all15plus" = case_m15plus + case_f15plus, "all_male" = case_m04 + case_m514 + case_m15plus, "all_female" = case_f04 + case_f514 + case_f15plus, "all_cases" = all_male + all_female)

all_copy <- all_tots
all_copy04 <- all_copy[, c(1:3)] 
colnames(all_copy04) <- c("year", "male", "female")
all_copy04 <- pivot_longer(all_copy04, cols = c(male, female), names_to = "sex", values_to = "cases")

all_copy514 <- all_copy[, c(1,4,5)] 
colnames(all_copy514) <- c("year", "male", "female")
all_copy514 <- pivot_longer(all_copy514, cols = c(male, female), names_to = "sex", values_to = "cases")

all_copy15plus <- all_copy[, c(1,6,7)] 
colnames(all_copy15plus) <- c("year", "male", "female")
all_copy15plus <- pivot_longer(all_copy15plus, cols = c(male, female), names_to = "sex", values_to = "cases")

all_copy15plus <- all_copy[, c(1,6,7)] 
colnames(all_copy15plus) <- c("year", "male", "female")
all_copy15plus <- pivot_longer(all_copy15plus, cols = c(male, female), names_to = "sex", values_to = "cases")

all_copyall <- all_copy[, c(1,11,12)] 
colnames(all_copyall) <- c("year", "male", "female")
all_copyall <- pivot_longer(all_copyall, cols = c(male, female), names_to = "sex", values_to = "cases")

###Drawing the plots 
world_04 <- ggplot(all_copy04, aes(x = year, y = cases, fill = sex)) + geom_bar(stat = "identity", position = "dodge") + labs(title = "Number of Reported Cases Amongst 0-4 yrs from 2013 to 2020")

world_514 <- ggplot(all_copy514, aes(x = year, y = cases, fill = sex)) + geom_bar(stat = "identity", position = "dodge") + labs(title = "Number of Reported Cases Amongst 5-14 yrs from 2013 to 2020")

world_15plus <- ggplot(all_copy15plus, aes(x = year, y = cases, fill = sex)) + geom_bar(stat = "identity", position = "dodge") + labs(title = "Number of Reported Cases Amongst 15plus yrs from 2013 to 2020")

world_all <- ggplot(all_copyall, aes(x = year, y = cases, fill = sex)) + geom_bar(stat = "identity", position = "dodge") + labs(title = "Number of Reported Cases Amongst All Ages from 2013 to 2020")

```

Repeating the continental difference between actual and predicted and including column for overall 

```{r}
#extracting data for whole world 
summed_m04 <- tot_world %>% group_by(year) %>% na.omit()
summed_m04 <- summarise(summed_m04, total = sum(newrel_m04))
colnames(summed_m04) <- c("year", "m04")

summed_f04 <- tot_world %>% group_by(year) %>% na.omit()
summed_f04 <- summarise(summed_f04, total = sum(newrel_f04))
colnames(summed_f04) <- c("year", "f04")

summed_m514 <- tot_world %>% group_by(year) %>% na.omit()
summed_m514 <- summarise(summed_m514, total = sum(newrel_m514))
colnames(summed_m514) <- c("year", "m514")

summed_f514 <- tot_world %>% group_by(year) %>% na.omit()
summed_f514 <- summarise(summed_f514, total = sum(newrel_f514))
colnames(summed_f514) <- c("year", "f514")

summed_m15plus <- tot_world %>% group_by(year) %>% na.omit()
summed_m15plus <- summarise(summed_m15plus, total = sum(newrel_m15plus))
colnames(summed_m15plus) <- c("year", "m15plus")

summed_f15plus <- tot_world %>% group_by(year) %>% na.omit()
summed_f15plus <- summarise(summed_f15plus, total = sum(newrel_f15plus))
colnames(summed_f15plus) <- c("year", "f15plus")

all_summed <- cbind(summed_m04, summed_f04$f04, summed_m514$m514, summed_f514$f514, summed_m15plus$m15plus, summed_f15plus$f15plus)
colnames(all_summed) <- c("year", "m04", "f04", "m514", "f514", "m15plus", "f15plus")

##Attempt at creating forecast 
arima_allm04 <- all_summed[-8, 2]
ts_arimam04 <- ts(arima_allm04, start = 2013, frequency = 1)
ts_arimam04 %<>% auto.arima(d=1, D=1, stepwise = FALSE, approximation = FALSE, trace = TRUE)
forecast_m04 <- forecast(ts_arimam04, h = 1)
autoplot(forecast_m04)
sum_m04 <- print(summary(forecast_m04))

arima_allf04 <- all_summed[-8, 3]
ts_arimaf04 <- ts(arima_allf04, start = 2013, frequency = 1)
ts_arimaf04 %<>% auto.arima(d=1, D=1, stepwise = FALSE, approximation = FALSE, trace = TRUE)
forecast_f04 <- forecast(ts_arimaf04, h = 1)
autoplot(forecast_f04)
sum_f04 <- print(summary(forecast_f04))

arima_allm514 <- all_summed[-8, 4]
ts_arimam514 <- ts(arima_allm514, start = 2013, frequency = 1)
ts_arimam514 %<>% auto.arima(d=1, D=1, stepwise = FALSE, approximation = FALSE, trace = TRUE)
forecast_m514 <- forecast(ts_arimam514, h = 1)
autoplot(forecast_m514)
sum_m514 <- print(summary(forecast_m514))

arima_allf514 <- all_summed[-8, 5]
ts_arimaf514 <- ts(arima_allf514, start = 2013, frequency = 1)
ts_arimaf514 %<>% auto.arima(d=1, D=1, stepwise = FALSE, approximation = FALSE, trace = TRUE)
forecast_f514 <- forecast(ts_arimaf514, h = 1)
autoplot(forecast_f514)
sum_f514 <- print(summary(forecast_f514))

arima_allm15plus <- all_summed[-8, 6]
ts_arimam15plus <- ts(arima_allm15plus, start = 2013, frequency = 1)
ts_arimam15plus %<>% auto.arima(d=1, D=1, stepwise = FALSE, approximation = FALSE, trace = TRUE)
forecast_m15plus <- forecast(ts_arimam15plus, h = 1)
autoplot(forecast_m15plus)
sum_m15plus <- print(summary(forecast_m15plus))

arima_allf15plus <- all_summed[-8, 7]
ts_arimaf15plus <- ts(arima_allf15plus, start = 2013, frequency = 1)
ts_arimaf15plus %<>% auto.arima(d=1, D=1, stepwise = FALSE, approximation = FALSE, trace = TRUE)
forecast_f15plus <- forecast(ts_arimaf15plus, h = 1)
autoplot(forecast_f15plus)
sum_f15plus <- print(summary(forecast_f15plus))

##Creating single data frame with best estimates for each group 
best_estimates <- cbind(sum_m04$`Point Forecast`, sum_f04$`Point Forecast`, sum_m514$`Point Forecast`, sum_f514$`Point Forecast`, sum_m15plus$`Point Forecast`, sum_f15plus$`Point Forecast`)
colnames(best_estimates) <- c("m04", "f04", "m514", "f514", "m15plus", "f15plus")

all_compared <- rbind(all_summed[8,-1], best_estimates)
rownames(all_compared) <- c("Actual", "Predicted")

all_differences <- t(all_compared)
all_differences %<>% as.data.frame()
all_differences <- mutate(all_differences, Percentage = 100*((Actual-Predicted)/Predicted))


sex <- c("m", "f")
add_col1 <- cbind(all_differences, sex)
age_group <- c("04", "04", "514", "514", "15plus", "15plus")
add_col2 <- cbind(add_col1, age_group)
g_whoregion <- "ALL"
add_col3 <- cbind(add_col2, g_whoregion)

#Ready to merge 
ready_all <- add_col3[, c(5,4,6,3)]
colnames(ready_all)[colnames(ready_all) == "Percentage"] <- "value"

piv_four <- rbind(piv_three, ready_all)
piv_four <- piv_four %>% arrange(age_group) %>% group_by(age_group) %>% arrange(sex)
piv_four$g_whoregion <- factor(piv_four$g_whoregion, levels = c("AFR", "AMR", "EMR", "EUR", "SEA", "WPR", "ALL"))

arima_four <- ggplot(piv_four, aes(x = g_whoregion, y = value, fill = sex)) + facet_wrap(~piv_four$age_group) + geom_bar(stat = "identity", position = "dodge") + theme(axis.text.x = element_text(angle = 90)) + theme(legend.title = element_blank()) + xlab("Region") + ylab("Percentage Difference Between Actual and Predicted Cases in 2020") + labs(title = "Difference Between Actual and Predicted Notifications in 2020")

arima_four 
```


